Soil Chemical Analysis

Review of the internet of things communication technologies in smart agriculture and challenges

Heavy metal pollution is emerging at a faster rate in non-contaminated areas, which are near to metropolitan cities. The ever-growing population with higher growth rate, industrial extension and poor management of natural resources across the globe in developing countries, particularly in India, make environments vulnerable to metal contamination. Over the last few decades, owing to scarcity of good quality water, farmers are forced to utilize marginal quality water for irrigation purpose which has led to reduction in soil health crop production potential. Long-term application of industrial effluent are accumulating significant amounts of heavy metals in soils and reach to human body via food chain contamination. It causes different types of ill effect in human being. Among heavy metals, lead (Pb) occupies the top place as priority pollutant as per classification given by the Central Pollution Control Board. Due to its toxicity in human body, poor development of infants, mental weakness, nerve disorder, kidney damage, gastric problem, hormonal imbalance and cardiovascular disease are common. The main sources of Pb contamination are acid batteries, paint and varnish, coal industries, automobile sectors, plastic and agriculture sectors. Lack of proper technological intervention in respect to waste water treatment or its safe disposal, enhances the more chance of Pb contamination in soil and water bodies. In nature, it is present as Pb2+, different types of oxide and hydroxides, and oxyanionic complexes; their chemistry is affected by various soil and water parameters. Safe disposal and remediation of Pb-contaminated soils by using traditional and modern tools and techniques is the need of the day for sustainable crop production.

Lead Contamination and Its Dynamics in Soil–Plant System

Deep Learning (DL) and Machine Learning (ML) are effectively utilized in various complicated challenges in healthcare, industry, and academia. The Internet of Drones (IoD) has lately cropped up due to high adjustability to a broad range of unpredictable circumstances. In addition, Unmanned Aerial Vehicles (UAVs) could be utilized efficiently in a multitude of scenarios, including rescue missions and search, farming, mission-critical services, surveillance systems, and so on, owing to technical and realistic benefits such as low movement, the capacity to lengthen wireless coverage zones, and the ability to attain places unreachable to human beings. In many studies, IoD and UAV are utilized interchangeably. Besides, drones enhance the efficiency aspects of various network topologies, including delay, throughput, interconnectivity, and dependability. Nonetheless, the deployment of drone systems raises various challenges relating to the inherent unpredictability of the wireless medium, the high mobility degrees, and the battery life that could result in rapid topological changes. In this paper, the IoD is originally explained in terms of potential applications and comparative operational scenarios. Then, we classify ML in the IoD-UAV world according to its applications, including resource management, surveillance and monitoring, object detection, power control, energy management, mobility management, and security management. This research aims to supply the readers with a better understanding of (1) the fundamentals of IoD/UAV, (2) the most recent developments and breakthroughs in this field, (3) the benefits and drawbacks of existing methods, and (4) areas that need further investigation and consideration. The results suggest that the Convolutional Neural Networks (CNN) method is the most often employed ML method in publications. According to research, most papers are on resource and mobility management. Most articles have focused on enhancing only one parameter, with the accuracy parameter receiving the most attention. Also, Python is the most commonly used language in papers, accounting for 90% of the time. Also, in 2021, it has the most papers published.

Machine Learning Applications in Internet-of-Drones: Systematic Review, Recent Deployments, and Open Issues

Weeds are among the most harmful abiotic factors in agriculture, triggering significant yield loss worldwide. Remote sensing can detect and map the presence of weeds in various spectral, spatial, and temporal resolutions. This review aims to show the current and future trends of UAV applications in weed detection in the crop field. This study systematically searched the original articles published from 1 January 2016 to 18 June 2021 in the databases of Scopus, ScienceDirect, Commonwealth Agricultural Bureaux (CAB) Direct, and Web of Science (WoS) using Boolean string: “weed” AND “Unmanned Aerial Vehicle” OR “UAV” OR “drone”. Out of the papers identified, 144 eligible studies did meet our inclusion criteria and were evaluated. Most of the studies (i.e., 27.42%) on weed detection were carried out during the seedling stage of the growing cycle for the crop. Most of the weed images were captured using red, green, and blue (RGB) camera, i.e., 48.28% and main classification algorithm was machine learning techniques, i.e., 47.90%. This review initially highlighted articles from the literature that includes the crops’ typical phenology stage, reference data, type of sensor/camera, classification methods, and current UAV applications in detecting and mapping weed for different types of crop. This study then provides an overview of the advantages and disadvantages of each sensor and algorithm and tries to identify research gaps by providing a brief outlook at the potential areas of research concerning the benefit of this technology in agricultural industries. Integrated weed management, coupled with UAV application improves weed monitoring in a more efficient and environmentally-friendly way. Overall, this review demonstrates the scientific information required to achieve sustainable weed management, so as to implement UAV platform in the real agricultural contexts.

How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields

Agriculture is undoubtedly one of the biggest and most important professions in the world. Optimization of agriculture and aiming gradually and extensively toward smart agriculture are the need of the hour. IOT (Internet of Things) technology has already been successful in easing people’s lives with its wide range of applications in almost all arenas. In this paper, our work takes the help of IOT devices, wireless sensor network (WSN) and AI techniques and combines them for faster and effective recommendation of suitable crops to farmers based on a list of factors such as temperature, annual precipitation, total available land size, past crop grown history and other resources. Additionally, detection of unwanted plants on crops, namely weed detection, is implemented with frame-capturing drone and deep learning methods. Naive Bayes algorithm for crop recommendation based on several factors detected by WSN sensor nodes has been used, resulting in an accuracy of 89.29%, which has proved to be better than several other discussed algorithms in the paper, like regression or support vector machine. Deep learning using neural network successfully identifies weeds present in a specific area of crop growth extending an additional protective measure to farmers. The comprehensive application developed for farmers not only reduces the physical hardship and time spent on different agricultural activities, but also increases the overall land yield, reduces possibility of losses due to failure of crops in a particular soil and lessens the chances of damage caused to crops by weeds.

AI Crop Predictor and Weed Detector Using Wireless Technologies: A Smart Application for Farmers

In peri-urban areas, the use of wastewater for crop production is a common practice due to water scarcity. Moreover, in the recent years, large quantity of wastewater generation and discharge as industrial effluent in water resources is another issue for reduction of water quality. The leather industries are significantly contributing chromium (Cr) in effluent, whereas, other industries may have salt and cationic load in their discharges are mixed up. Therefore, it is mandatory to study the interactive effect of different effluent constituents on crop plants. In this connection, a pot culture experiment was conducted at the ICAR-Indian Institute of Soil Science, Bhopal to compute the effect of application of calcium (Ca) and sodium (Na) ions on Cr uptake by spinach crop in Vertisol of central India. Three levels of Cr (0, 50, 100 mg kg−1), calcium (0, 2, 4 mM), and sodium (0, 40, 80 mM) were applied in combinations. The spinach variety All Green was used as a test crop and harvested at full maturity. Results showed that application of Ca and Na reduced the Cr uptake in spinach crop. The reduction of Cr uptake was more in the root than shoot. Applied calcium acted as an essential plant nutrient and enhanced the crop biomass. Sole applications of Na adversely affected the crop biomass and Cr uptake in both root and shoot of spinach. In conclusion, application of Ca fertilizers reduced the Cr toxicity in spinach and could be used as a strategy for the safe utilization of tannery industrial effluents for crop production.

Reducing chromium uptake through application of calcium and sodium in spinach.

Long-term application of sewage for crop production accumulates significant amount of heavy metals in soil Some of these metals are carcinogenic and affect the soil, plant, animal and human health adversely Phytoremediation with mustard is a well known and regularly adopted practice for metal removal from contaminated areas However, genetic potential of mustard cultivars may have much influence on phytoremediation potential of mustard For this, four prominent mustard crop varieties (NRC DR 2, NRC HB 101, RH 749 and RH 119) were grown at sewage contributed metal contaminated agriculture fields of Islamnagar and Bheropura villages Results showed that all the mustard varieties removed significant amount of Cu, Cd, Pb, Cr, Ni, Zn and Mn from soil Mean data of all three locations showed that maximum removal of Cu (479 mg plant-1), Cd (280 mg plant-1), Cr (381 mg plant-1), Ni (996 mg plant-1), Zn (266 mg plant-1) and Mn (538 mg plant-1) was in NRC HB 101 except for Pb uptake which was highest in NRC DR 2 (153 mg plant-1) The NRC HB 101 showed higher phytoextraction potential compared to rest of the varieties These results are very much useful for formulation of phytoremediation management strategies under metal contaminated agriculture areas

Comparative Evaluation of Phytoremediation Potential of Indian Mustard (Brassica juncea) Varieties Under Sewage Irrigated Sites

Environmental Impact Measurements: Tool and Techniques

Soil quality assessment has been suggested as a tool for evaluating sustainability of soil and crop management practices. The study was conducted to assess the soil quality of AESR 10.1 covering largely Vertisols using 15 physical, chemical and biological attributes. The results showed that 70 and 28% of the soils of Sehore district had organic C content in medium and low range while 76% of the soils were low in organic C content in Vidisha district. In both the districts, more than 80 and 50% soils had available N and P status in medium range and more than 75% soils were medium in available K status. The relative soil quality index (RSQI) was computed for each district based on 15 soil attributes, which are known to exert significant influence on crop productivity. The soil with RSQI values less than 50% were rated as poor, 5070% as medium category and more than 70% as good quality soils. About 4, 78 and 18% soil samples of Sehore and 16, 78 and 66% soil samples of Vidisha districts belonged to poor, medium and good quality categories, respectively. The RSQI values were significantly correlated with average relative yield (r = 0.636**), indicating the reliability of the proposed method of soil quality assessment.

J. K. Saha

Papers

AI Crop Predictor and Weed Detector Using Wireless Technologies: A Smart Application for Farmers

Reducing chromium uptake through application of calcium and sodium in spinach.

Comparative Evaluation of Phytoremediation Potential of Indian Mustard (Brassica juncea) Varieties Under Sewage Irrigated Sites

Environmental Impact Measurements: Tool and Techniques

Assessing Soil Health of Vertisol of AESR 10.1 using Selected Physical, Chemical and Biological Attributes of Soils