Showing papers on "Precision agriculture published in 2014"

Nanotechnology in agri-food production: an overview.

Abstract: Nanotechnology is one of the most important tools in modern agriculture, and agri-food nanotechnology is anticipated to become a driving economic force in the near future. Agri-food themes focus on sustainability and protection of agriculturally produced foods, including crops for human consumption and animal feeding. Nanotechnology provides new agrochemical agents and new delivery mechanisms to improve crop productivity, and it promises to reduce pesticide use. Nanotechnology can boost agricultural production, and its applications include: 1) nanoformulations of agrochemicals for applying pesticides and fertilizers for crop improvement; 2) the application of nanosensors/nanobiosensors in crop protection for the identification of diseases and residues of agrochemicals; 3) nanodevices for the genetic manipulation of plants; 4) plant disease diagnostics; 5) animal health, animal breeding, poultry production; and 6) postharvest management. Precision farming techniques could be used to further improve crop yields but not damage soil and water, reduce nitrogen loss due to leaching and emissions, as well as enhance nutrients long-term incorporation by soil microorganisms. Nanotechnology uses include nanoparticle-mediated gene or DNA transfer in plants for the development of insect-resistant varieties, food processing and storage, nanofeed additives, and increased product shelf life. Nanotechnology promises to accelerate the development of biomass-to-fuels production technologies. Experts feel that the potential benefits of nanotechnology for agriculture, food, fisheries, and aquaculture need to be balanced against concerns for the soil, water, and environment and the occupational health of workers. Raising awareness of nanotechnology in the agri-food sector, including feed and food ingredients, intelligent packaging and quick-detection systems, is one of the keys to influencing consumer acceptance. On the basis of only a handful of toxicological studies, concerns have arisen regarding the safety of nanomaterials, and researchers and companies will need to prove that these nanotechnologies do not have more of a negative impact on the environment.

Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System

Abstract: Precision Farming (PF) management strategies are commonly based on estimations of within-field yield potential, often derived from remotely-sensed products, e.g., Vegetation Index (VI) maps. These well-established means, however, lack important information, like crop height. Combinations of VI-maps and detailed 3D Crop Surface Models (CSMs) enable advanced methods for crop yield prediction. This work utilizes an Unmanned Aircraft System (UAS) to capture standard RGB imagery datasets for corn grain yield prediction at three early- to mid-season growth stages. The imagery is processed into simple VI-orthoimages for crop/non-crop classification and 3D CSMs for crop height determination at different spatial resolutions. Three linear regression models are tested on their prediction ability using site-specific (i) unclassified mean heights, (ii) crop-classified mean heights and (iii) a combination of crop-classified mean heights with according crop coverages. The models show determination coefficients \({R}^{2}\) of up to 0.74, whereas model (iii) performs best with imagery captured at the end of stem elongation and intermediate spatial resolution (0.04m\(\cdot\)px\(^{-1}\)).Following these results, combined spectral and spatial modeling, based on aerial images and CSMs, proves to be a suitable method for mid-season corn yield prediction.

Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat

Abstract: High spatial resolution images taken by unmanned aerial vehicles (UAVs) have been shown to have the potential for monitoring agronomic and environmental variables. However, it is necessary to capture a large number of overlapped images that must be mosaicked together to produce a single and accurate ortho-image (also called an ortho-mosaicked image) representing the entire area of work. Thus, ground control points (GCPs) must be acquired to ensure the accuracy of the mosaicking process. UAV ortho-mosaics are becoming an important tool for early site-specific weed management (ESSWM), as the discrimination of small plants (crop and weeds) at early growth stages is subject to serious limitations using other types of remote platforms with coarse spatial resolutions, such as satellite or conventional aerial platforms. Small changes in flight altitude are crucial for low-altitude image acquisition because these variations can cause important differences in the spatial resolution of the ortho-images. Furthermore, a decrease of flying altitude reduces the area covered by each single overlapped image, which implies an increase of both the sequence of images and the complexity of the image mosaicking procedure to obtain an ortho-image covering the whole study area. This study was carried out in two wheat fields naturally infested by broad-leaved and grass weeds at a very early phenological stage. The geometric accuracy differences and crop line alignment among ortho-mosaics created from UAV image series were investigated while taking into account three different flight altitudes (30, 60 and 100 m) and a number of GCPs (from 11 to 45). The results did not show relevant differences in geo-referencing accuracy on the interval of altitudes studied. Similarly, the increase of the number of GCPs did not imply a relevant increase of geo-referencing accuracy. Therefore, the most important parameter to consider when choosing the flying altitude is the ortho-image spatial resolution required rather than the geo-referencing accuracy. Regarding the crop mis-alignment, the results showed that the overall errors were less than twice the spatial resolution, which did not break the crop line continuity at the studied spatial resolutions (pixels from 7.4 to 24.7 mm for 30, 60 and 100 m flying altitudes respectively) on the studied crop (early wheat). The results lead to the conclusion that a UAV flying at a range of 30 to 100 m altitude and using a moderate number of GCPs is able to generate ultra-high spatial resolution ortho-imagesortho-images with the geo-referencing accuracy required to map small weeds in wheat at a very early phenological stage. This is an ambitious agronomic objective that is being studied in a wide research program whose global aim is to create broad-leaved and grass weed maps in wheat crops for an effective ESSWM.

Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice

Abstract: Appropriate field management requires methods of measuring plant height with high precision, accuracy, and resolution. Studies show that terrestrial laser scanning (TLS) is suitable for capturing small objects like crops. In this contribution, the results of multitemporal TLS surveys for monitoring plant height on paddy rice fields in China are presented. Three campaigns were carried out on a field experiment and on a farmer’s conventionally managed field. The high density of measurement points allows us to establish crop surface models with a resolution of 1 cm, which can be used for deriving plant heights. For both sites, strong correlations (each R 2 =0.91 between TLS-derived and manually measured plant heights confirm the accuracy of the scan data. A biomass regression model was established based on the correlation between plant height and biomass samples from the field experiment (R 2 =0.86 ). The transferability to the farmer’s field was supported with a strong correlation between simulated and measured values (R 2 =0.90 ). Independent biomass measurements were used for validating the temporal transferability. The study demonstrates the advantages of TLS for deriving plant height, which can be used for modeling biomass. Consequently, laser scanning methods are a promising tool for precision agriculture.

A Crop/Weed Field Image Dataset for the Evaluation of Computer Vision Based Precision Agriculture Tasks

Abstract: In this paper we propose a benchmark dataset for crop/weed discrimination, single plant phenotyping and other open computer vision tasks in precision agriculture. The dataset comprises 60 images with annotations and is available online (http://github.com/cwfid). All images were acquired with the autonomous field robot Bonirob in an organic carrot farm while the carrot plants were in early true leaf growth stage. Intra- and inter-row weeds were present, weed and crop were approximately of the same size and grew close together. For every dataset image we supply a ground truth vegetation segmentation mask and manual annotation of the plant type (crop vs. weed). We provide initial results for the phenotyping problem of crop/weed classification and propose evaluation methods to allow comparison of different approaches. By opening this dataset to the community we want to stimulate research in this area where the current lack of public datasets is one of the barriers for progress.

Precision Crop Protection - the Challenge and Use of Heterogeneity

Abstract: Precision farming is an agricultural management system using global navigation satellite systems, geographic information systems, remote sensing, and data management systems for optimizing the use of nutrients, water, seed, pesticides and energy in heterogeneous field situations. This book provides extensive information on the state-of-the-art of research on precision crop protection and recent developments in site-specific application technologies for the management of weeds, arthropod pests, pathogens and nematodes. It gives the reader an up-to-date and in-depth review of both basic and applied research developments. The chapters discuss I) biology and epidemiology of pests, II) new sensor technologies, III) applications of multi-scale sensor systems, IV) sensor detection of pests in growing crops, V) spatial and non-spatial data management, VI) impact of pest heterogeneity and VII) precise mechanical and chemical pest control.

Wireless Sensor Network in precision agriculture application

Abstract: The Wireless Sensors Network (WSN) is nowadays widely used to build decision support systems to overcome many problems in the real-world. One of the most interesting fields having an increasing need of decision support systems is precision agriculture (PA). This paper presents WSN as the best way to solve the agricultural problems related to farming resources optimization, decision making support, and land monitoring. This approach provides real-time information about the lands and crops that will help farmers make right decisions. Using the basic principles of Internet and WSN technology, precision agriculture systems based on the internet of things (IOT) technology is explained in detail especially on the hardware architecture, network architecture and software process control of the precision irrigation system. The software monitors data from the sensors in a feedback loop which activates the control devices based on threshold value. Implementation of WSN in PA will optimize the usage of water fertilizer and also maximized the yield of the crops.

Design and development of precision agriculture system using wireless sensor network

Abstract: Crop farming in India is labour intensive and obsolete. Farming is still dependent on techniques which were evolved hundreds of years ago and doesn't take care of conservation of resources. The newer scenario of decreasing water tables, drying up of rivers and tanks, unpredictable environment present an urgent need of proper utilization of water. We have the technology to bridge the gap between water usage and water wastage. Technology used in some developed countries is too expensive and complicated for a common farmer to understand. Our project is to give cheap, reliable, cost efficient and easy to use technology which would help in conservation of resources such as water and also in automatizing farms. We proposed use of temperature and moisture sensor at suitable locations for monitoring of crops. The sensing system is based on a feedback control mechanism with a centralized control unit which regulates the flow of water on to the field in the real time based on the instantaneous temperature and moisture values. The sensor data would be collected in a central processing unit which would take further action. Thus by providing right amount of water we would increase the efficiency of the farm. The farmer can also look at the sensory data and decide course of action himself. We have made the interface of our project keeping in view the educational and financial background of average Indian farmer. In this paper we are proposed a low cost and efficient wireless sensor network technique to acquire the soil moisture and temperature from various locations of farm and as per the need of crop controller take the decision to make irrigation ON or OFF.

An Effective Method for Crop Monitoring Using Wireless Sensor Network

Abstract: The main aim of this paper is to propose a state of art wireless sensor technology in agriculture, which can show the path to the rural farming community to replace some of the traditional techniques. In this project, the sensor motes have several external sensors namely leaf wetness, soil moisture, soil pH, atmospheric pressure sensors attached to it. Based on the value of soil moisture sensor the mote triggers the water sprinkler during the period of water scarcity. Once the field is sprinkled with adequate water, the water sprinkler is switched off. Hereby water can be conserved. Also the value of soil pH sensor is sent to the base station and in turn base station intimates the farmer about the soil pH via SMS using GSM modem. Obtaining the soil pH value in his mobile the farmer selects the necessary fertilizer and crop for his next season. Hereby the amount of fertilizer can be reduced. In order to overcome the lack of information and technical support and to increase the rice production, a development of rice cropping monitoring using WSN is proposed to provide a helping hand to farmers in real-time monitoring, achieving precision agriculture and thus increasing the rice production. Thus automated control of water sprinkling and ultimate supply of information to farmers is done as a result of this project using wireless sensor network.

Combining hyperspectral UAV and mulitspectral FORMOSAT-2 imagery for precision agriculture applications

Abstract: Precision agriculture requires detailed information regarding the crop status variability within a field. Remote sensing provides an efficient way to obtain such information through observing biophysical parameters, such as canopy nitrogen content, leaf coverage, and plant biomass. However, individual remote sensing sensors often fail to provide information which meets the spatial and temporal resolution required by precision agriculture. The purpose of this study is to investigate methods which can be used to combine imagery from various sensors in order to create a new dataset which comes closer to meeting these requirements. More specifically, this study combined multispectral satellite imagery (Formosat-2) and hyperspectral Unmanned Aerial Vehicle (UAV) imagery of a potato field in the Netherlands. The imagery from both platforms was combined in two ways. Firstly, data fusion methods brought the spatial resolution of the Formosat-2 imagery (8 m) down to the spatial resolution of the UAV imagery (1 m). Two data fusion methods were applied: an unmixing-based algorithm and the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The unmixing-based method produced vegetation indices which were highly correlated to the measured LAI (rs= 0.866) and canopy chlorophyll values (rs=0.884), whereas the STARFM obtained lower correlations. Secondly, a Spectral-Temporal Reflectance Surface (STRS) was constructed to interpolate a daily 101 band reflectance spectra using both sources of imagery. A novel STRS method was presented, which utilizes Bayesian theory to obtain realistic spectra and accounts for sensor uncertainties. The resulting surface obtained a high correlation to LAI (rs=0.858) and canopy chlorophyll (rs=0.788) measurements at field level. The multi-sensor datasets were able to characterize significant differences of crop status due to differing nitrogen fertilization regimes from June to August. Meanwhile, the yield prediction models based purely on the vegetation indices extracted from the unmixing-based fusion dataset explained 52.7% of the yield variation, whereas the STRS dataset was able to explain 72.9% of the yield variability. The results of the current study indicate that the limitations of each individual sensor can be largely surpassed by combining multiple sources of imagery, which is beneficial for agricultural management. Further research could focus on the integration of data fusion and STRS techniques, and the inclusion of imagery from additional sensors.

Smart irrigation using low-cost moisture sensors and XBee-based communication

Abstract: Deficiency in fresh water resources globally has raised serious alarms in the last decade. Efficient management of water resources play an important role in the agriculture sector. Unfortunately, this is not given prime importance in the third world countries because of adhering to traditional practices. This paper presents a smart system that uses a bespoke, low cost soil moisture sensor to control water supply in water deficient areas. The sensor, which works on the principle of moisture dependent resistance change between two points in the soil, is fabricated using affordable materials and methods. Moisture data acquired from a sensor node is sent through XBEE wireless communication modules to a centralized server that controls water supply. A user-friendly interface is developed to visualize the daily moisture data. The low-cost and wireless nature of the sensing hardware presents the possibility to monitor the moisture levels of large agricultural fields. Moreover, the proposed moisture sensing method has the ability to be incorporated into an automated drip-irrigation scheme and perform automated, precision agriculture in conjunction with de-centralized water control.

Decision Support Systems to Manage Irrigation in Agriculture

Abstract: A decision support system (DSS) is an interactive software-based system used to help decision-makers compile useful information from a combination of raw data, documents, and personal knowledge; to identify and solve problems; and to make an optimized decision. The DSS architecture consists of the database (or knowledge base), the model (i.e., the decision context and user criteria), and the user interface. The main advantages of using a DSS include examination of multiple alternatives, better understanding of the processes, identification of unpredicted situations, enhanced communication, cost effectiveness, and better use of data and resources. The application DSS in agriculture and environment has been rapidly increased in the past decade, which allows rapid assessment of agricultural production systems around the world and decision-making at both farm and district levels, though constraints exist for successful adoption of this technology in agriculture. One of the important applications of DSS in agriculture is water management at both field and district levels. Agriculture is facing more severe and growing competition with other sectors for freshwater. The water resources are becoming increasingly insufficient to meet the demand in developing countries and their quality is declining due to pollution and inadequate management. Irrigation is an effective means to enhance crop productions, but water needs to be supplied accurately, taking into account its availability, crop requirement and land size, irrigation systems, and crop productivity and feasibility. This chapter attempts to present the state-of-art principles, design, and application of DSS in agriculture, particularly irrigation practices, and to identify emerging approaches and future direction of research in this field.

Effects of Reduced Terrestrial LiDAR Point Density on High-Resolution Grain Crop Surface Models in Precision Agriculture

Abstract: 3D geodata play an increasingly important role in precision agriculture, e.g., for modeling in-field variations of grain crop features such as height or biomass. A common data capturing method is LiDAR, which often requires expensive equipment and produces large datasets. This study contributes to the improvement of 3D geodata capturing efficiency by assessing the effect of reduced scanning resolution on crop surface models (CSMs). The analysis is based on high-end LiDAR point clouds of grain crop fields of different varieties (rye and wheat) and nitrogen fertilization stages (100%, 50%, 10%). Lower scanning resolutions are simulated by keeping every n-th laser beam with increasing step widths n. For each iteration step, high-resolution CSMs (0.01 m2 cells) are derived and assessed regarding their coverage relative to a seamless CSM derived from the original point cloud, standard deviation of elevation and mean elevation. Reducing the resolution to, e.g., 25% still leads to a coverage of >90% and a mean CSM elevation of >96% of measured crop height. CSM types (maximum elevation or 90th-percentile elevation) react differently to reduced scanning resolutions in different crops (variety, density). The results can help to assess the trade-off between CSM quality and minimum requirements regarding equipment and capturing set-up.

Deployment of wireless sensor networks (WSN) in automated irrigation management and scheduling systems: a review

Abstract: Wireless sensor networks (WSN) is an important and exciting technology with great potential for application in various fields including medicine, transportation, agriculture, industrial process control, global-scale environmental monitoring and precision agriculture. This paper reviews the application of WSN in automating irrigation management and rescheduling. Irrigation management and rescheduling based on WSN are potential solutions for optimum water management via automatic access to in-field soil moisture conditions and control of irrigation systems

Topsoil moisture estimation for precision agriculture using unmmaned aerial vehicle multispectral imagery

Abstract: There is an increasing trend in crop production management decisions in precision agriculture based on observation of high resolution aerial images from unmanned aerial vehicles (UAV). Nevertheless, there are still limitations in terms of relating the spectral imagery information to the agricultural targets. AggieAir™ is a small, autonomous unmanned aircraft which carries multispectral cameras to capture aerial imagery during pre-programmed flights. AggieAir enables users to gather imagery at greater spatial and temporal resolution than most manned aircraft and satellite sources. The platform has been successfully used in support of a wide variety of water and natural resources management areas. This paper presents results of an on-going research in the application of the imagery from AggieAir in the remote sensing of top soil moisture estimations for a large field served by a center pivot sprinkler irrigation system.

A review of precision fertilization research

Abstract: Precision agriculture plays an important role in sustainable development. Precision fertilization is the core of this field. Three methods of precision fertilization are introduced in this paper. They are: testing soil for formulated fertilization technology, decision support system and expert decision support system. Their development situations are also examined. Some suggestions in the agriculture sustainable development are also provided. The review summarized that 3S technology will become the main data source in the decision support system and in development process of expert decision support system. The MIII technology can raise the soil testing efficiency. The development of precision fertilization has great utility in sustainable development of agriculture.

A survey of image processing techniques for agriculture

Abstract: Computer technologies have been shown to improve agricultural productivity in a number of ways. One technique which is emerging as a useful tool is image processing. This paper presents a short survey on using image processing techniques to assist researchers and farmers to improve agricultural practices. Image processing has been used to assist with precision agriculture practices, weed and herbicide technologies, monitoring plant growth and plant nutrition management. This paper highlights the future potential for image processing for different agricultural industry contexts.

WSN sensors for precision agriculture

Abstract: The application of technology in the field of agriculture has increased the effectiveness and efficiency of the farmers. The application of Wireless Sensor Network (WSN) in precision agriculture assists the farmers to know about their fields in statistical manner, which helps them in making better and accurate decisions. There are various type of sensors that can be used to calculate the statistical parameters of an agricultural fields, which convert the event or a phenomenon into an electrical or measurable quantity. This paper provides an elaboration of the basic principles of some of the sensors and their related specifications of few commercial products.

Farmers’ Perception of Precision Farming Technology among Hungarian Farmers

Abstract: Many technologies have appeared in agriculture to reduce the harmful effects of chemical use. One of these technologies is precision farming technology. Precision farming technology should not be considered as only the latest plant production technology or only a new agro-management tool. It is achieved only when the results of electronics and IT equipment are realized in the variable rate treatments zone-by-zone. The advantages and disadvantages of this technology highly depend on the heterogeneity of soil, the knowledge and attitude of the manager and the staff. This is the reason why opinions about the technology effects are so wide. This paper shows the results of the investigation based on interviews about the adoption and knowledge of precision farming technology among Hungarian crop producers. This technology is mostly used by farms over 300 hectares with young farmers. The most characteristic elements were precision fertilization and tractor guidance. The survey examined three groups of farmers with respect to whether they apply precision farming elements or not. We refer to them as “users”, “planners” and “non-users”. According to the survey, the opinions of the “user” and the “non-user” groups of farmers are not significantly different regarding the impacts of precision farming technology (the main advantages were the change in yield quantity, chemical usage and income). Furthermore, the opinions of the farmers regarding the changes in variable costs resulting from the adoption of precision farming technology were also examined (measured in percent). Box-plot analysis was used for this examination. According to the opinion of the “user” group of farmers, the highest cost savings occurred in fertilizer and herbicide costs.

Deployment and Performance Study of an Ad Hoc Network Protocol for Intelligent Video Sensing in Precision Agriculture

Abstract: Recent advances in technology applied to agriculture have made possible the Precision Agriculture (PA). It has been widely demonstrated that precision agriculture provides higher productivity with lower costs. The goal of this paper is to show the deployment of a real-time precision sprayer which uses video sensing captured by lightweight UAVs (unmanned aerial vehicles) forming ad hoc network. It is based on a geo-reference system that takes into account weeds inside of a mapped area. The ad hoc network includes devices such as AR Drones, a laptop and a sprayer in a tractor. The experiment was carried out in a corn field with different locations selected to represent the diverse densities of weeds that can be found in the field. The deployed system allows saving high percentage of herbicide, reducing the cost spent in fertilizers and increasing the quality of the product.

Survey of Thermal Infrared Remote Sensing for Unmanned Aerial Systems

Abstract: As Unmanned Aerial System (UAS) technology matures, the list of potential civilian applications continues to grow substantially. Currently, the majority of applications are centered around providing optical imagery, either in real-time video or high resolution mapping. But as more sophisticated applications are desired, the limitations of simple imagery are becoming more evident, especially for precision agricultural applications. However, recent advancements in UAS based precision agriculture have demonstrated the effectiveness of including thermal infrared (TIR) cameras. In many situations, decision support indicators are evident in the TIR spectrum, whereas they are undetectable in the visible light and near-infrared spectrum. In this paper, a survey of some of the applications under development utilizing TIR imagery is presented along with implementation strategies to provide guidance for researchers wishing to add TIR imagery into their applications.

Future Directions for Automated Weed Management in Precision Agriculture

Abstract: In cropping systems, integrated weed management is based on diversification. Rather than relying solely on one or two herbicides, a multiplicity of weed control strategies is employed. Yet, integrated weed management as currently practiced is far from integrated; every weed is still managed the same regardless of location or season. The recent development of precision application technology is now allowing for smaller treatment units by making applications according to site-specific demands. The automated systems of the future will have sensor and computer technologies that first categorize each and every plant in the field as either weed or crop and then identify the species of weed. Following identification, multiple weed control tools located on a single platform are applied at micro-rates to individual plants based on their biology. For example, if the system identified a weed resistant to Roundup, it could be spritzed with a different herbicide or nipped with an onboard cutter or singed with a burst of flame. This system and others like it will be capable of targeting different weed-killing tools to specific weeds. This chapter will discuss the challenges and tools of the future.