Showing papers in "Agronomy in 2022"
TL;DR: A Reversible Automatic Selection Normalization (RASN) network is proposed, integrating the normalization and renormalization layer to evaluate and select thenormalization module of the prediction model, showing good prediction ability and adaptability for the greenhouse in the Smart Agriculture System.
Abstract: Due to the nonlinear modeling capabilities, deep learning prediction networks have become widely used for smart agriculture. Because the sensing data has noise and complex nonlinearity, it is still an open topic to improve its performance. This paper proposes a Reversible Automatic Selection Normalization (RASN) network, integrating the normalization and renormalization layer to evaluate and select the normalization module of the prediction model. The prediction accuracy has been improved effectively by scaling and translating the input with learnable parameters. The application results of the prediction show that the model has good prediction ability and adaptability for the greenhouse in the Smart Agriculture System.
70 citations
TL;DR: In this paper , six versions of the You Only Look Once (YOLO) object detection algorithm were evaluated for real-time bunch detection and counting in grapes, and the best combination of accuracy and speed was achieved by YOLOv4-tiny.
Abstract: Over the last few years, several Convolutional Neural Networks for object detection have been proposed, characterised by different accuracy and speed. In viticulture, yield estimation and prediction is used for efficient crop management, taking advantage of precision viticulture techniques. Convolutional Neural Networks for object detection represent an alternative methodology for grape yield estimation, which usually relies on manual harvesting of sample plants. In this paper, six versions of the You Only Look Once (YOLO) object detection algorithm (YOLOv3, YOLOv3-tiny, YOLOv4, YOLOv4-tiny, YOLOv5x, and YOLOv5s) were evaluated for real-time bunch detection and counting in grapes. White grape varieties were chosen for this study, as the identification of white berries on a leaf background is trickier than red berries. YOLO models were trained using a heterogeneous dataset populated by images retrieved from open datasets and acquired on the field in several illumination conditions, background, and growth stages. Results have shown that YOLOv5x and YOLOv4 achieved an F1-score of 0.76 and 0.77, respectively, with a detection speed of 31 and 32 FPS. Differently, YOLO5s and YOLOv4-tiny achieved an F1-score of 0.76 and 0.69, respectively, with a detection speed of 61 and 196 FPS. The final YOLOv5x model for bunch number, obtained considering bunch occlusion, was able to estimate the number of bunches per plant with an average error of 13.3% per vine. The best combination of accuracy and speed was achieved by YOLOv4-tiny, which should be considered for real-time grape yield estimation, while YOLOv3 was affected by a False Positive–False Negative compensation, which decreased the RMSE.
69 citations
TL;DR: In this article , the impacts of drought stress on soil microbial community abundance, structure and activity, and plant growth and development, including the role of soil microorganisms in this process were discussed.
Abstract: Nowadays, the most significant consequence of climate change is drought stress. Drought is one of the important, alarming, and hazardous abiotic stresses responsible for the alterations in soil environment affecting soil organisms, including microorganisms and plants. It alters the activity and functional composition of soil microorganisms that are responsible for crucial ecosystem functions and services. These stress conditions decrease microbial abundance, disturb microbial structure, decline microbial activity, including enzyme production (e.g., such as oxidoreductases, hydrolases, dehydrogenase, catalase, urease, phosphatases, β-glucosidase) and nutrient cycling, leading to a decrease in soil fertility followed by lower plant productivity and loss in economy. Interestingly, the negative effects of drought on soil can be minimized by adding organic substances such as compost, sewage slugs, or municipal solid waste that increases the activity of soil enzymes. Drought directly affects plant morphology, anatomy, physiology, and biochemistry. Its effect on plants can also be observed by changes at the transcriptomic and metabolomic levels. However, in plants, it can be mitigated by rhizosphere microbial communities, especially by plant growth-promoting bacteria (PGPB) and fungi (PGPF) that adapt their structural and functional compositions to water scarcity. This review was undertaken to discuss the impacts of drought stress on soil microbial community abundance, structure and activity, and plant growth and development, including the role of soil microorganisms in this process. Microbial activity in the soil environment was considered in terms of soil enzyme activities, pools, fluxes, and processes of terrestrial carbon (C) and nitrogen (N) cycles. A deep understanding of many aspects is necessary to explore the impacts of these extreme climate change events. We also focus on addressing the possible ways such as genome editing, molecular analysis (metagenomics, transcriptomics, and metabolomics) towards finding better solutions for mitigating drought effects and managing agricultural practices under harsh condition in a profitable manner.
54 citations
TL;DR: This review paper made an attempt to critically review biosurfactants, their usage, research related to them, and challenges faced.
Abstract: With the present climate change and increasing world population, there is an urgent need to discover creative, efficient, and cost-effective natural products for the benefit of humanity. Biosurfactants are produced by various microorganisms that have several distinct properties compared to other synthetic surfactants, including mild production conditions, multifunctionality, higher biodegradability, and lower toxicity of living cells synthesis of active compounds. Due to their surface tension reducing, emulsion stabilizing, and biodegrading properties of these in place of chemical surfactants, they are generating huge demand in terms of research and usage. Biosurfactants are widely used in the food industry as food-formulation ingredients and antiadhesive agents as emulsifiers, de-emulsifiers, spreading agents, foaming agents, and detergents that find application in various fields such as agriculture, industrial sectors, and environmental recreation. Recent research focused more on heavy metal bioremediation from compost was achieved using biosurfactants-producing bacteria, which resulted in an improvement in compost quality. Although a number of studies on biosurfactants synthesis have been reported, very limited information on its cinematics and the consumption of renewable substrates are available. In this review paper, we made an attempt to critically review biosurfactants, their usage, research related to them, and challenges faced.
46 citations
TL;DR: In this article , the authors evaluate smart agriculture using IoT approaches in depth and find existing techniques that may be used to boost crop yield and save time, such as water, pesticides, irrigation, crop, and fertilizer management.
Abstract: With the rise of new technologies, such as the Internet of Things, raising the productivity of agricultural and farming activities is critical to improving yields and cost-effectiveness. IoT, in particular, can improve the efficiency of agriculture and farming processes by eliminating human intervention through automation. The fast rise of Internet of Things (IoT)-based tools has changed nearly all life sectors, including business, agriculture, surveillance, etc. These radical developments are upending traditional agricultural practices and presenting new options in the face of various obstacles. IoT aids in collecting data that is useful in the farming sector, such as changes in climatic conditions, soil fertility, amount of water required for crops, irrigation, insect and pest detection, bug location disruption of creatures to the sphere, and horticulture. IoT enables farmers to effectively use technology to monitor their forms remotely round the clock. Several sensors, including distributed WSNs (wireless sensor networks), are utilized for agricultural inspection and control, which is very important due to their exact output and utilization. In addition, cameras are utilized to keep an eye on the field from afar. The goal of this research is to evaluate smart agriculture using IoT approaches in depth. The paper demonstrates IoT applications, benefits, current obstacles, and potential solutions in smart agriculture. This smart agricultural system aims to find existing techniques that may be used to boost crop yield and save time, such as water, pesticides, irrigation, crop, and fertilizer management.
45 citations
TL;DR: In this article , the authors provide a holistic framework for sustainable crop and weed management to reduce the use and risk of chemical pesticides by 50% by 2030, although it is still undefined whether a reduction in herbicide use could be feasible in different farming systems and situations.
Abstract: Agricultural systems in the EU have become more vulnerable and less sustainable due to an overreliance on herbicides and the tremendous increase in herbicide-resistant weeds. The EU Green Deal aims to reduce the use and risk of chemical pesticides by 50% by 2030, although it is still undefined whether a reduction in herbicide use could be feasible in different farming systems and situations. This review aims to provide a holistic framework for sustainable crop and weed management to reduce the herbicide input and ensure crop protection. Current and future dilemmas and policies that need to be handled to ensure the agroecological transition of the EU’s agricultural systems are also discussed. The integration of non-chemical alternatives for integrated weed management is feasible and includes novel cultivation techniques (e.g., intercropping, false seedbed, reduced tillage, crop rotation and diversification, adjustments on sowing densities and dates), non-chemical tools (e.g., flaming, seed coating, beneficial microorganisms, mechanical weeding, biocontrol agents and natural herbicides), competitive plant material (hybrids and cultivars, cover crops, service crops), and new technologies and precision agriculture tools (e.g., Decision Support Systems, robots, remote sensing, UAVs, omics and nanotechnology). A special focus should be appointed to agroecology and biodiversity conservation.
43 citations
TL;DR: In this paper , a study was conducted with the aim to screen different wheat genotypes based on stress tolerance levels, and the results indicated that screening for drought-tolerant genotypes may be a more viable option to minimize drought-induced effects on wheat in drought-prone regions.
Abstract: Water scarcity is a major challenge to wheat productivity under changing climate conditions, especially in arid and semi-arid regions. During recent years, different agronomic, physiological and molecular approaches have been used to overcome the problems related to drought stress. Breeding approaches, including conventional and modern breeding, are among the most efficient options to overcome drought stress through the development of new varieties adapted to drought. Growing drought-tolerant wheat genotypes may be a sustainable option to boost wheat productivity under drought stress conditions. Therefore, the present study was conducted with the aim to screen different wheat genotypes based on stress tolerance levels. For this purpose, eleven commonly cultivated wheat genotypes (V1 = Akbar-2019, V2 = Ghazi-2019, V3 = Ujala-2016, V4 = Zincol-2016, V5 = Anaj-2017, V6 = Galaxy-2013, V7 = Pakistan-2013, V8 = Seher-2006, V9 = Lasani-2008, V10 = Faisalabad-2008 and V11 = Millat-2011) were grown in pots filled with soil under well-watered (WW, 70% of field capacity) and water stress (WS, 35% of field capacity) conditions. Treatments were arranged under a completely randomized design (CRD) with three replicates. Data on yield and yield-related traits (tillers/plant, spikelets/spike, grains/spike, 100 grain weight, seed and biological yield) and physio-biochemical (chlorophyll contents, relative water content, membrane stability index, leaf nitrogen, phosphorus, and potassium content) attributes were recorded in this experiment. Our results showed that drought stress significantly affected the morpho-physiological, and biochemical attributes in all tested wheat varieties. Among the genotypes, all traits were found to be significantly (p < 0.05) higher in wheat genotype Faisalabad-2008, including biological yield (9.50 g plant−1) and seed yield (3.39 g plant−1), which was also proven to be more drought tolerant than the other tested genotypes. The higher biological and grain yield of genotype Faisalabad-2008 was mainly attributed to greater numbers of tillers/plant and spikelets/spike compared to the other tested genotypes. The wheat genotype Galaxy-2013 had significantly lower biological (7.43 g plant−1) and seed yield (2.11 g plant−1) than all other tested genotypes, and was classified as a drought-sensitive genotype. For the genotypes, under drought stress, biological and grain yield decreased in the order V10 > V2 > V1 > V4 > V7 > V11 > V9 > V8 > V3 > V6. These results suggest that screening for drought-tolerant genotypes may be a more viable option to minimize drought-induced effects on wheat in drought-prone regions.
42 citations
TL;DR: In this article , a long-close distance coordination control strategy for a litchi picking robot was proposed based on an Intel Realsense D435i camera combined with a point cloud map collected by the camera.
Abstract: For the automated robotic picking of bunch-type fruit, the strategy is to roughly determine the location of the bunches, plan the picking route from a remote location, and then locate the picking point precisely at a more appropriate, closer location. The latter can reduce the amount of information to be processed and obtain more precise and detailed features, thus improving the accuracy of the vision system. In this study, a long-close distance coordination control strategy for a litchi picking robot was proposed based on an Intel Realsense D435i camera combined with a point cloud map collected by the camera. The YOLOv5 object detection network and DBSCAN point cloud clustering method were used to determine the location of bunch fruits at a long distance to then deduce the sequence of picking. After reaching the close-distance position, the Mask RCNN instance segmentation method was used to segment the more distinctive bifurcate stems in the field of view. By processing segmentation masks, a dual reference model of “Point + Line” was proposed, which guided picking by the robotic arm. Compared with existing studies, this strategy took into account the advantages and disadvantages of depth cameras. By experimenting with the complete process, the density-clustering approach in long distance was able to classify different bunches at a closer distance, while a success rate of 88.46% was achieved during fruit-bearing branch locating. This was an exploratory work that provided a theoretical and technical reference for future research on fruit-picking robots.
32 citations
TL;DR: YOLO-Banana detection model has the best performance with good detection accuracy for banana bunches and stalks in the natural environment as discussed by the authors , and the network is lightweight and has good real-time performance and application prospects in intelligent management and automatic harvesting in the banana orchard.
Abstract: The real-time detection of banana bunches and stalks in banana orchards is a key technology in the application of agricultural robots. The complex conditions of the orchard make accurate detection a difficult task, and the light weight of the deep learning network is an application trend. This study proposes and compares two improved YOLOv4 neural network detection models in a banana orchard. One is the YOLO-Banana detection model, which analyzes banana characteristics and network structure to prune the less important network layers; the other is the YOLO-Banana-l4 detection model, which, by adding a YOLO head layer to the pruned network structure, explores the impact of a four-scale prediction structure on the pruning network. The results show that YOLO-Banana and YOLO-Banana-l4 could reduce the network weight and shorten the detection time compared with YOLOv4. Furthermore, YOLO-Banana detection model has the best performance, with good detection accuracy for banana bunches and stalks in the natural environment. The average precision (AP) values of the YOLO-Banana detection model on banana bunches and stalks are 98.4% and 85.98%, and the mean average precision (mAP) of the detection model is 92.19%. The model weight is reduced from 244 to 137 MB, and the detection time is shortened from 44.96 to 35.33 ms. In short, the network is lightweight and has good real-time performance and application prospects in intelligent management and automatic harvesting in the banana orchard.
30 citations
TL;DR: The survey aims at providing an overview of the current use of copper-based plant-protection products in European organic agriculture and the need for alternatives to allow policymakers to develop strategies for a complete phasing out.
Abstract: The reduction of copper-based plant-protection products with the final aim of phasing out has a high priority in European policy, as well as in organic agriculture. Our survey aims at providing an overview of the current use of these products in European organic agriculture and the need for alternatives to allow policymakers to develop strategies for a complete phasing out. Due to a lack of centralized databases on pesticide use, our survey combines expert knowledge on permitted and real copper use per crop and country, with statistics on organic area. In the 12 surveyed countries (Belgium, Bulgaria, Denmark, Estonia, France, Germany, Hungary, Italy, Norway, Spain, Switzerland, and the UK), we calculated that approximately 3258 t copper metal per year is consumed by organic agriculture, equaling to 53% of the permitted annual dosage. This amount is split between olives (1263 t y−1, 39%), grapevine (990 t y−1, 30%), and almonds (317 t y−1, 10%), followed by other crops with much smaller annual uses (<80 t y−1). In 56% of the allowed cases (countries × crops), farmers use less than half of the allowed amount, and in 27%, they use less than a quarter. At the time being, completely abandoning copper fungicides would lead to high yield losses in many crops. To successfully reduce or avoid copper use, all preventive strategies have to be fully implemented, breeding programs need to be intensified, and several affordable alternative products need to be brought to the market.
28 citations
TL;DR: This review primarily focuses on low-temperature stress experienced by plants and their strategies to overcome it and has reviewed recent progress and previous knowledge for a better understanding of plant cold stress response.
Abstract: Cold stress has always been a significant limitation for plant development and causes substantial decreases in crop yield. Some temperate plants, such as Arabidopsis, have the ability to carry out internal adjustment, which maintains and checks the metabolic machinery during cold temperatures. This cold acclimation process requires prior exposure to low, chilling temperatures to prevent damage during subsequent freezing stress and maintain the overall wellbeing of the plant despite the low-temperature conditions. In comparison, plants of tropical and subtropical origins, such as rice, are sensitive to chilling stress and respond differently to low-temperature stress. Plants have evolved various physiological, biochemical, and molecular mechanisms to sense and respond to low-temperature stress, including membrane modifications and cytoskeletal rearrangement. Moreover, the transient increase in cytosolic calcium level leads to the activation of many calcium-binding proteins and calcium-dependent protein kinases during low-temperature stress. Recently, mitogen-activated protein kinases have been found to regulate low-temperature signaling through ICE1. Besides, epigenetic control plays a crucial role during the cold stress response. This review primarily focuses on low-temperature stress experienced by plants and their strategies to overcome it. We have also reviewed recent progress and previous knowledge for a better understanding of plant cold stress response.
TL;DR: This paper proposes the use of DL models (SSD MobileNet v2 and YOLOv4) to efficiently detect the tomatoes and compares those systems with a proposed histogram-based HSV colour space model to classify each tomato and determine its ripening stage, through two image datasets acquired.
Abstract: The harvesting operation is a recurring task in the production of any crop, thus making it an excellent candidate for automation. In protected horticulture, one of the crops with high added value is tomatoes. However, its robotic harvesting is still far from maturity. That said, the development of an accurate fruit detection system is a crucial step towards achieving fully automated robotic harvesting. Deep Learning (DL) and detection frameworks like Single Shot MultiBox Detector (SSD) or You Only Look Once (YOLO) are more robust and accurate alternatives with better response to highly complex scenarios. The use of DL can be easily used to detect tomatoes, but when their classification is intended, the task becomes harsh, demanding a huge amount of data. Therefore, this paper proposes the use of DL models (SSD MobileNet v2 and YOLOv4) to efficiently detect the tomatoes and compare those systems with a proposed histogram-based HSV colour space model to classify each tomato and determine its ripening stage, through two image datasets acquired. Regarding detection, both models obtained promising results, with the YOLOv4 model standing out with an F1-Score of 85.81%. For classification task the YOLOv4 was again the best model with an Macro F1-Score of 74.16%. The HSV colour space model outperformed the SSD MobileNet v2 model, obtaining results similar to the YOLOv4 model, with a Balanced Accuracy of 68.10%.
TL;DR: In this paper , the most common conventional and non-conventional weed control strategies from a sustainability perspective, highlighting the application of the precision and automated weed control technologies associated with precision weed management (PWM).
Abstract: In the last few decades, the increase in the world’s population has created a need to produce more food, generating, consequently, greater pressure on agricultural production. In addition, problems related to climate change, water scarcity or decreasing amounts of arable land have serious implications for farming sustainability. Weeds can affect food production in agricultural systems, decreasing the product quality and productivity due to the competition for natural resources. On the other hand, weeds can also be considered to be valuable indicators of biodiversity because of their role in providing ecosystem services. In this sense, there is a need to carry out an effective and sustainable weed management process, integrating the various control methods (i.e., cultural, mechanical and chemical) in a harmonious way, without harming the entire agrarian ecosystem. Thus, intensive mechanization and herbicide use should be avoided. Herbicide resistance in some weed biotypes is a major concern today and must be tackled. On the other hand, the recent development of weed control technologies can promote higher levels of food production, lower the amount of inputs needed and reduce environmental damage, invariably bringing us closer to more sustainable agricultural systems. In this paper, we review the most common conventional and non-conventional weed control strategies from a sustainability perspective, highlighting the application of the precision and automated weed control technologies associated with precision weed management (PWM).
TL;DR: In this paper , a literature review shows that crop rotation can effectively enhance climate resilience and reduce the fragility of agricultural cropping systems, which is an important tool for improving the climate resilience of the agricultural production system and effectively solving the shortcomings of the current continuous crop methodology.
Abstract: In the context of climate change, increases in extreme weather have caused a series of problems, severely reduced crop yield, and caused a loss of agricultural cultivation. In addition, because of the high economic benefits, continuous cropping has become more popular but it leads to higher land-use intensity in production systems, aggravating the problems of extreme climate and seriously influencing China’s agricultural production and ecological environment. From this, the importance of improvements to cropping systems’ resilience to climate change is now much clearer than before. Crop rotation is an important tool for improving the climate resilience of the agricultural production system and effectively solving the shortcomings of the current continuous crop methodology. Crop rotation is indispensable in many national strategies, including food security, ecological environment development, and rural revitalization. This study aimed to promote the improvement of the crop rotation system in China and aimed to play a significant role in guiding China towards the large-scale development of crop rotation. This literature review shows that crop rotation can effectively enhance climate resilience and reduce the fragility of agricultural cropping systems. It then delves into the origin and development of crop rotation, and summarizes the characteristics of crop rotation. In view of the neglect of ecological benefits in China’s agricultural development, this article puts forward three suggestions: first, developing crop rotation technology based on local conditions; second, paying attention to the ecological benefits of crop rotation subsidies, followed by implementing appropriate and flexible subsidy policies; and, finally, carrying out rational evaluations and policy adjustment of crop rotation practices.
TL;DR: A review of the current knowledge on GB involvement in plant thermotolerance can be found in this article , where the authors discuss knowledge gaps and future research directions for enhancing thermotolerant in economically important crop plants.
Abstract: As global warming progresses, agriculture will likely be impacted enormously by the increasing heat stress (HS). Hence, future crops, especially in the southern Mediterranean regions, need thermotolerance to maintain global food security. In this regard, plant scientists are searching for solutions to tackle the yield-declining impacts of HS on crop plants. Glycine betaine (GB) has received considerable attention due to its multiple roles in imparting plant abiotic stress resistance, including to high temperature. Several studies have reported GB as a key osmoprotectant in mediating several plant responses to HS, including growth, protein modifications, photosynthesis, gene expression, and oxidative defense. GB accumulation in plants under HS differs; therefore, engineering genes for GB accumulation in non-accumulating plants is a key strategy for improving HS tolerance. Exogenous application of GB has shown promise for managing HS in plants, suggesting its involvement in protecting plant cells. Even though overexpressing GB in transgenics or exogenously applying it to plants induces tolerance to HS, this phenomenon needs to be unraveled under natural field conditions to design breeding programs and generate highly thermotolerant crops. This review summarizes the current knowledge on GB involvement in plant thermotolerance and discusses knowledge gaps and future research directions for enhancing thermotolerance in economically important crop plants.
TL;DR: In this paper , MGT gene family members were identified and characterized into two species of Cucurbitaceae, including Cucumis sativus and Citrullus lanatus.
Abstract: Magnesium transporters (MGTs) play a prominent role in the absorption, transportation, and storage of magnesium in plant cells. In the present study, MGT gene family members were identified and characterized into two species of Cucurbitaceae, including Cucumis sativus and Citrullus lanatus. Totals of 20 and 19 MGT genes were recognized in Citrullus lanatus and Cucumis sativus, respectively. According to their physicochemical properties, the members of each sub-class of MGTs in the species of Cucurbitaceae showed the close relationship. Proteins from NIPA class were identified as hydrophilic proteins with high stability. Based on phylogenetic analysis, MGT family members were classified into three groups, and NIPAs showed more diversity. Moreover, duplication events were not identified between the MGT genes in C. lanatus and C. sativus. According to pocket analysis, residues such as L, V, S, I, and A were frequently observed in the binding sites of MGT proteins in both studied species. The prediction of post-translation modifications revealed that MSR2 proteins have higher phosphorylation potentials than other sub-classes of MGT in both studied plants. The expression profile of MGTs showed that MGTs are more expressed in root tissues. In addition, MGTs showed differential expression in response to abiotic/biotic stresses as well as hormone application and NIPAs were more induced in response to stimuli in watermelon. The results of this study, as the primary work of MGT gene family, can be used in programs related to Cucurbitaceae breeding.
TL;DR: In this paper , the authors used an ANN to estimate the reference crop evapotranspiration (ETo) based on limited meteorological data using an artificial neural network (ANN) method.
Abstract: Reference crop evapotranspiration (ETo) is an important component of the hydrological cycle that is used for water resource planning, irrigation, and agricultural management, as well as in other hydrological processes. The aim of this study was to estimate the ETo based on limited meteorological data using an artificial neural network (ANN) method. The daily data of minimum temperature (Tmin), maximum temperature (Tmax), mean temperature (Tmean), solar radiation (SR), humidity (H), wind speed (WS), sunshine hours (Ssh), maximum global radiation (gradmax), minimum global radiation (gradmin), day length, and ETo data were obtained over the long-term period from 1969 to 2019. The analysed data were divided into two parts from 1969 to 2007 and from 2008 to 2019 for model training and testing, respectively. The optimal ANN for forecasting ETo included Tmax, Tmin, H, and SR at hidden layers (4, 3); gradmin, SR, and WS at (6, 4); SR, day length, Ssh, and Tmean at (3, 2); all collected parameters at hidden layer (5, 4). The results showed different alternative methods for estimation of ETo in case of a lack of climate data with high performance. Models using ANN can help promote the decision-making for water managers, designers, and development planners.
TL;DR: In this paper , the authors applied different footprinting approaches (carbon footprint (CF), nitrogen footprint (NF), water footprint (WF)) and determined the economic return on organic farming (OF) and conventional rice farming (CVF) at the farm scale.
Abstract: An integrated method is required for comprehensive assessment of the environmental impacts and economic benefits of rice production systems. Therefore, the objective of this study was to apply different footprinting approaches (carbon footprint (CF), nitrogen footprint (NF), water footprint (WF)) and determine the economic return on organic farming (OF) and conventional rice farming (CVF) at the farm scale. Over the 4-year study period (2018–2021), the results showed lower net greenhouse gas (GHG) emissions in OF (3289.1 kg CO2eq ha−1 year−1) than in CVF (4921.7 kg CO2eq ha−1 year−1), indicating that the use of OF can mitigate the GHG emissions from soil carbon sequestration. However, there was a higher CF intensity in OF (1.17 kg CO2eq kg−1 rice yield) than in CVF (0.93 kg CO2eq kg−1 rice yield) due to the lower yield. The NF intensities of OF and CVF were 0.34 and 11.94 kg Neq kg−1 rice yield, respectively. The total WF of CVF (1470.1 m3 ton−1) was higher than that in OF (1216.3 m3 ton−1). The gray water in CVF was significantly higher than that in OF due to the use of chemical fertilizers, herbicides, and pesticides. Although the rice yield in OF was nearly two times lower than that in CVF, the economic return was higher due to lower production costs and higher rice prices. However, more field studies and long-term monitoring are needed for future research.
TL;DR: In this article , the effect of varying degrees of drought stress on potato growth, development, and yield has been explored, and the current potato production scenario and the effect on potato genotype has been discussed.
Abstract: Potato is the third most consumed crop globally after rice and wheat. It is a short-duration crop, versatile in use, suitable for growing in a wide range of environments, and its production is increasing rapidly. The modern potato is considered a drought-sensitive crop, and it is susceptible to yield loss because of drought stress. Unfortunately, drought severity, frequency, and extent have been increasing around the globe because of climate change. Potato drought susceptibility has primarily been attributed to its shallow root system. However, several studies in past decades have suggested that drought susceptibility of potato also depends upon the type, developmental stage, and the morphology of the genotype, and the duration and severity of drought stress. They have been overlooked, and root depth is considered the only significant cause of potato drought susceptibility. This review combines these studies to understand the varying response of potato genotypes. This review also explores the current potato production scenario and the effect of varying degrees of drought stress on potatoes’ growth, development, and yield. In the absence of drought-tolerant genotypes, agronomic practices should be improved to mitigate drought stress. Late maturing cultivars, nutrient management, mulching, and foliar application of plant growth regulators can be used during prolonged droughts. Irrigation at tuber initiation and the tuber bulking stage during early droughts can reduce the adverse effects of drought.
TL;DR: A systematic literature review applying the PRISMA protocol and develops a framework that summarizes the main challenges encountered, machine learning techniques, and the leading technologies used in agricultural Big Data.
Abstract: Agricultural Big Data is a set of technologies that allows responding to the challenges of the new data era. In conjunction with machine learning, farmers can use data to address problems such as farmers’ decision making, water management, soil management, crop management, and livestock management. Crop management includes yield prediction, disease detection, weed detection, crop quality, and species recognition. On the other hand, livestock management considers animal welfare and livestock production. The purpose of this paper is to synthesize the evidence regarding the challenges involved in implementing machine learning in agricultural Big Data. We conducted a systematic literature review applying the PRISMA protocol. This review includes 30 papers published from 2015 to 2020. We develop a framework that summarizes the main challenges encountered, machine learning techniques, and the leading technologies used. A significant challenge is the design of agricultural Big Data architectures due to the need to modify the set of technologies adapting the machine learning techniques as the volume of data increases.
TL;DR: A systematic review integrated with a bibliometric analysis of several agronomic practices that increase common bean yield and quality was conducted, based on the literature published during 1971-2021 as discussed by the authors .
Abstract: Common bean (Phaseolus vulgaris L.) is the most important legume for human consumption worldwide and an important source of vegetable protein, minerals, antioxidants, and bioactive compounds. The N2-fixation capacity of this crop reduces its demand for synthetic N fertilizer application to increase yield and quality. Fertilization, yield, and quality of common bean may be optimised by several other agronomic practices such as irrigation, rhizobia application, sowing density, etc. Taking this into consideration, a systematic review integrated with a bibliometric analysis of several agronomic practices that increase common bean yield and quality was conducted, based on the literature published during 1971–2021. A total of 250 publications were found dealing with breeding (n = 61), sowing density and season (n = 14), irrigation (n = 36), fertilization (n = 27), intercropping (n = 12), soilless culture (n = 5), tillage (n = 7), rhizobia application (n = 36), biostimulant/biofertilizer application (n = 21), disease management (n = 15), pest management (n = 2) and weed management (n = 14). The leading research production sites were Asia and South America, whereas from the Australian continent, only four papers were identified as relevant. The keyword co-occurrence network analyses revealed that the main topics addressed in relation to common bean yield in the scientific literature related to that of “pod”, “grain”, “growth”, “cultivar” and “genotype”, followed by “soil”, “nitrogen”, “inoculation”, “rhizobia”, “environment”, and “irrigation”. Limited international collaboration among scientists was found, and most reported research was from Brazil. Moreover, there is a complete lack in interdisciplinary interactions. Breeding for increased yield and selection of genotypes adapted to semi-arid environmental conditions combined with the suitable sowing densities are important agronomic practices affecting productivity of common bean. Application of fertilizers and irrigation practices adjusted to the needs of the plants according to the developmental stage and selection of the appropriate tillage system are also of high importance to increase common bean yield and yield qualities. Reducing N-fertilization via improved N-fixation through rhizobia inoculation and/or biostimulants application appeared as a main consideration to optimise crop performance and sustainable management of this crop. Disease and weed management practices appear neglected areas of research attention, including integrated pest management.
TL;DR: In this paper , the benefits of organic or synthetic mulches for crop production, as well as the uses of mulching in soil and water conservation were discussed, and the use of organic and biodegradable mulches was dominated by organic materials, while inorganic mulches are mostly comprised of plastic-based components.
Abstract: This research was carried out in order to demonstrate that mulching the ground helps to conserve water, because agricultural sustainability in dryland contexts is threatened by drought, heat stress, and the injudicious use of scarce water during the cropping season by minimizing surface evaporation. Improving soil moisture conservation is an ongoing priority in crop outputs where water resources are restricted and controlled. One of the reasons for the desire to use less water in agriculture is the rising demand brought on by the world’s growing population. In this study, the use of organic or biodegradable mulches was dominated by organic materials, while inorganic mulches are mostly comprised of plastic-based components. Plastic film, crop straw, gravel, volcanic ash, rock pieces, sand, concrete, paper pellets, and livestock manures are among the materials put on the soil surface. Mulching has several essential applications, including reducing soil water loss and soil erosion, enriching soil fauna, and improving soil properties and nutrient cycling in the soil. It also reduces the pH of the soil, which improves nutrient availability. Mulching reduces soil deterioration by limiting runoff and soil loss, and it increases soil water availability by reducing evaporation, managing soil temperature, or reducing crop irrigation requirements. This review paper extensively discusses the benefits of organic or synthetic mulches for crop production, as well as the uses of mulching in soil and water conservation. As a result, it is very important for farmers to choose mulching rather than synthetic applications.
TL;DR: In this paper , the role of chitosan (Ci) foliar application on morphological, physiological, biochemical, and anatomical parameters of calendula under water stress conditions was evaluated.
Abstract: Severe water stress conditions limit growth and development of floricultural crops which affects flower quality. Hence, development of effective approaches for drought tolerance is crucial to limit recurring water deficit challenges. Foliar application of various plant growth regulators has been evaluated to improve drought tolerance in different floricultural crops; however, reports regarding the role of chitosan (Ci) on seasonal flowers like calendula are still scant. Therefore, we evaluated the role of Ci foliar application on morphological, physiological, biochemical, and anatomical parameters of calendula under water stress conditions. Different doses of Ci (0, 2.5, 5, 7.5, 10 mg L−1) were applied through foliar application to evaluate their impact in enhancing growth and photosynthetic pigments of calendula. The optimized Ci level of 7.5 mg L−1 was further evaluated to study mechanisms of water stress tolerance in calendula. Ci application significantly increased biomass and pigments in calendula. Ci (7.5 mg L−1) resulted in increased photosynthetic rate (72.98%), transpiration rate (62.11%), stomatal conductance (59.54%), sub-stomatal conductance (20.62%), and water use efficiency (84.93%). Furthermore, it improved catalase, guaiacol peroxidase, and superoxide dismutase by 56.70%, 64.94%, and 32.41%, respectively. These results highlighted the significance of Ci in inducing drought tolerance in pot marigold.
TL;DR: In this paper , the effect of different photynthetic photon flux density (PPFD) provided by LEDs (Light Emitting Diodes) and photoperiod on biomass production, morphological traits, photosynthetic performance, sensory attributes, and image texture parameters of indoor cultivated romaine lettuce was evaluated.
Abstract: In this study, the effect of different photosynthetic photon flux density (PPFD) provided by LEDs (Light Emitting Diodes) and photoperiod on biomass production, morphological traits, photosynthetic performance, sensory attributes, and image texture parameters of indoor cultivated romaine lettuce was evaluated. Two cultivars of lettuce Lactuca sativa var. longifolium namely ‘Casual’ (Syngenta)—midi romaine lettuce with medium-compact heads—and ‘Elizium’ (Enza Zaden)—a mini type (Little Gem) with compact heavy heads—were used. PPFD of 160 and 240 µmol m−2 s−1 and photoperiod of 16 and 20 h were applied, and Daily Light Integral (DLI) values were 9.2, 11.5, 13.8, and 17.3 mol m−2 day−1. The experiment lasted 30 days in the Indoor Controlled Environment Agriculture facility. DLI equal to 17.3 mol m−2 per day for cv. ‘Casual’ and 11.5–17.3 mol m−2 per day for cv. ‘Elizium’ allowed to obtain a very high fresh weight, 350 and 240 g, respectively, within 30 days of cultivation in an indoor plant production facility. The application of the lowest PPFD 160 µmol m−2 s−1 and 16 h photoperiod (9.2 mol m−2 per day DLI) resulted in the lowest fresh weight, the number of leaves and head circumference. The level of nitrate, even at the lowest DLI, was below the limit imposed by European Community Regulation. The cv. ‘Elizium’ lettuce grown at PPFD 240 µmol m−2 s−1 and 16 h photoperiod had the highest overall sensory quality. The cv. ‘Casual’ lettuce grown at PPFD 160 µmol m−2 s−1 and 20 h photoperiod had the lowest sensory quality. The samples subjected to different photoperiod and PPFD were also successively distinguished in an objective and non-destructive way using image features and machine learning algorithms. The average accuracy for the leaf samples of cv. ‘Casual’ lettuce reached 98.75% and for cv. ‘Elizium’ cultivar—86.25%. The obtained relationship between DLI and yield, as well as the quality of romaine lettuce, can be used in practice to improve romaine lettuce production in an Indoor Controlled Environment.
TL;DR: In this article , a detailed review of the research studies that have been carried out during the last few years, with a specific focus on the technologies that allow for the enhancement of the system effectiveness under hot and arid conditions, and that decrease the energy and water consumption.
Abstract: This work is motivated by the difficulty of cultivating crops in horticulture greenhouses under hot and arid climate conditions. The main challenge is to provide a suitable greenhouse indoor environment, with sufficiently low costs and low environmental impacts. The climate control inside the greenhouse constitutes an efficient methodology for maintaining a satisfactory environment that fulfills the requirements of high-yield crops and reduced energy and water resource consumption. In hot climates, the cooling systems, which are assisted by an effective control technique, constitute a suitable path for maintaining an appropriate climate inside the greenhouse, where the required temperature and humidity distribution is maintained. Nevertheless, most of the commonly used systems are either highly energy or water consuming. Hence, the main objective of this work is to provide a detailed review of the research studies that have been carried out during the last few years, with a specific focus on the technologies that allow for the enhancement of the system effectiveness under hot and arid conditions, and that decrease the energy and water consumption. Climate control processes in the greenhouse by means of manual and smart control systems are investigated first. Subsequently, the different cooling technologies that provide the required ranges of temperature and humidity inside the greenhouse are detailed, namely, the systems using heat exchangers, ventilation, evaporation, and desiccants. Finally, the recommended energy-efficient approaches of the desiccant dehumidification systems for greenhouse farming are pointed out, and the future trends in cooling systems, which include water recovery using the method of combined evaporation–condensation, as well as the opportunities for further research and development, are identified as a contribution to future research work.
TL;DR: In this paper , the authors determined maize seeds' germination and seedling development under various abiotic stresses, including drought and waterlogging, using 30 water levels based on one-milliliter intervals and as percentages of thousand kernel weight (TKW) at 20 and 25 °C.
Abstract: Germination and seedling development are essential stages in a plant’s life cycle, greatly influenced by temperature and moisture conditions. The aim of this study was to determine maize (Zea mays L.) seeds’ germination and seedling development under various abiotic stresses. Eight different temperature levels, 5, 10, 15, 20, 25, 30, 35, and 40 °C, were used. Drought and waterlogging stresses were tested using 30 water levels based on one-milliliter intervals and as percentages of thousand kernel weight (TKW) at 20 and 25 °C. Seedling density and the use of antifungals were also examined. Temperature significantly affected germination duration and seedling growth, and 20 °C was found to be ideal with an optimal range of less than 30 °C. Germination occurred at 25% of the TKW. The optimal water range for seedling growth was higher and broader than the range for germination. Seed size assisted in defining germination water requirements and providing an accurate basis. The present research established an optimum water supply range of 150–325% of the TKW for maize seedling development. A total of 6 seeds per 9 cm Petri dish may be preferable over greater densities. The technique of priming seeds with an antifungal solution before planting was observed to have a better effect than applying it in the growth media.
TL;DR: In this paper , the authors used CNN-based pre-trained models for efficient plant disease identification and fine-tuned the hyperparameters of these models, such as DenseNet-121, ResNet-50, VGG-16, and Inception V4.
Abstract: The agricultural sector plays a key role in supplying quality food and makes the greatest contribution to growing economies and populations. Plant disease may cause significant losses in food production and eradicate diversity in species. Early diagnosis of plant diseases using accurate or automatic detection techniques can enhance the quality of food production and minimize economic losses. In recent years, deep learning has brought tremendous improvements in the recognition accuracy of image classification and object detection systems. Hence, in this paper, we utilized convolutional neural network (CNN)-based pre-trained models for efficient plant disease identification. We focused on fine tuning the hyperparameters of popular pre-trained models, such as DenseNet-121, ResNet-50, VGG-16, and Inception V4. The experiments were carried out using the popular PlantVillage dataset, which has 54,305 image samples of different plant disease species in 38 classes. The performance of the model was evaluated through classification accuracy, sensitivity, specificity, and F1 score. A comparative analysis was also performed with similar state-of-the-art studies. The experiments proved that DenseNet-121 achieved 99.81% higher classification accuracy, which was superior to state-of-the-art models.
TL;DR: In this article , a 30-day soil leaching experiment was carried out using a completely randomized design with 16 treatments and 3 replications after which the leached soil samples were used for a pH buffering capacity study.
Abstract: In the tropics, warm temperatures and high rainfall contribute to acidic soil formation because of the significant leaching of base cations (K+, Ca2+, Mg2+, and Na+), followed by the replacement of the base cations with Al3+, Fe2+, and H+ ions at the soil adsorption sites. The pH buffering capacity of highly weathered acid soils is generally low because of their low pH which negatively impacts soil and crop productivity. Thus, there is a need to amend these soils with the right amount of inorganic liming materials which have relatively high neutralizing values and reactivity to overcome the aforementioned problems. Soil leaching and the pH buffering capacity studies were conducted to determine whether the co-application or co-amendment of a calcium carbonate product (Calciprill) and sodium silicate can improve soil nutrient retention and pH buffering capacity of the Bekenu series (Typic Paleudults). A 30 day soil leaching experiment was carried out using a completely randomized design with 16 treatments and 3 replications after which the leached soil samples were used for a pH buffering capacity study. The Calciprill and sodium silicate treatments significantly improved soil pH, exchangeable NH4+, available P, exchangeable base cations, Effective Cation Exchange Capacity (ECEC), and pH buffering capacity in comparison with the untreated soil. The improvements were attributed to the alkalinity of Calciprill and sodium silicate due to their high inherent K+, Ca2+, Mg2+, and Na+ contents. The neutralizing effects of the amendments impeded the hydrolysis of Al3+ (96.5%), Fe2+ (70.4%), and Mn2+ (25.3%) ions resulting in fewer H+ ions being produced. The co-application of Calciprill and sodium silicate reduced the leaching of Ca2+ (58.7%) and NO3− (74.8%) from the amended soils. This was due to the ability of sodium silicate to reduce soil permeability and protect the Calciprill and available NO3− from being leached. This also improved the longevity of Calciprill to enhance the soil pH buffering capacity. However, the amounts of NH4+, P, and base cations leached from the amended soils were higher compared with the un-amended soils. This was due to the high solubility of sodium silicate. The most suitable combination amendment was 7.01 g Calciprill and 9.26 g sodium silicate (C2S5) per kilogram soil. It is possible for farmers to adopt the combined use Calciprill and sodium silicate to regulate soil nutrient retention and improve the soil pH buffering capacity of highly weathered acidic soils. This will enhance soil and crop productivity.
TL;DR: The interaction between soil organic carbon (SOC) and clay minerals is a critical mechanism for retaining SOC and protecting soil fertility and long-term agricultural sustainability as mentioned in this paper , which is the reason why the TS treatment promoted enrichment of clay in aggregates.
Abstract: The interaction between soil organic carbon (SOC) and clay minerals is a critical mechanism for retaining SOC and protecting soil fertility and long-term agricultural sustainability. The SOC composition and minerals speciation in clay fractions (<2 μm) within soil aggregates under straw removed (T) and straw incorporation (TS) conditions were analyzed by X-ray diffraction, Fourier transform infrared spectra and X-ray photoelectron spectroscopy. The TS treatment promoted enrichment of clay in aggregates. The TS increased the contents of SOC (27.0–86.6%), poorly crystalline Fe oxide (Feo), and activity of Fe oxides (Feo/Fed); whereas, it reduced the concentrations of free Fe oxide (Fed) in the clay fractions within aggregates. Straw incorporation promoted the accumulation of aromatic-C and carboxylic-C in the clay fraction within aggregates. The relative amount of hydroxy-interlayered vermiculite, aliphatic-C, and alcohol-C in the clay fractions within the macroaggregates was higher than that microaggregates, whereas the relative amounts of illite, kaolinite, Fe(III), and aromatic-C had a reverse tendency. The hydroxy-interlayered vermiculite in clay fractions showed positive correlation with the amounts of C–C(H) (r = 0.93) and C–O (r = 0.96 *, p < 0.05). The concentration of Feo and Feo/Fed ratio was positively correlated with the amounts of C=C and C(O)O content in clay within aggregates. Long-term straw incorporation induced transformation of clay minerals and Fe oxide, which was selectively stabilized straw-derived organic compounds in clay fractions within soil aggregates.
TL;DR: In this paper , a Smart-Map plugin was developed using modern artificial intelligence (AI) tools to generate interpolated maps, Ordinary Kriging (OK) and the Support Vector Machine (SVM) algorithm were implemented.
Abstract: Machine Learning (ML) algorithms have been used as an alternative to conventional and geostatistical methods in digital mapping of soil attributes. An advantage of ML algorithms is their flexibility to use various layers of information as covariates. However, ML algorithms come in many variations that can make their application by end users difficult. To fill this gap, a Smart-Map plugin, which complements Geographic Information System QGIS Version 3, was developed using modern artificial intelligence (AI) tools. To generate interpolated maps, Ordinary Kriging (OK) and the Support Vector Machine (SVM) algorithm were implemented. The SVM model can use vector and raster layers available in QGIS as covariates at the time of interpolation. Covariates in the SVM model were selected based on spatial correlation measured by Moran’s Index (I’Moran). To evaluate the performance of the Smart-Map plugin, a case study was conducted with data of soil attributes collected in an area of 75 ha, located in the central region of the state of Goiás, Brazil. Performance comparisons between OK and SVM were performed for sampling grids with 38, 75, and 112 sampled points. R2 and RMSE were used to evaluate the performance of the methods. SVM was found superior to OK in the prediction of soil chemical attributes at the three sample densities tested and was therefore recommended for prediction of soil attributes. In this case study, soil attributes with R2 values ranging from 0.05 to 0.83 and RMSE ranging from 0.07 to 12.01 were predicted by the methods tested.