Use In Precision Agriculture Research Articles

Prospects for “Smart Agriculture” in Russia

The authors justify the use of precision agriculture as a key vector in the development of the “smart agriculture” segment of the FoodNet platform of the national technological initiative. A prospectless extensive agriculture, based on exploitation of natural soil fertility, prevails in Russia; therefore, perennial field research has highlighted the economic and ecological advisability of using the information technologies of precision agriculture. Against the background of a significant increase in crop yields, the payback on fertilizers and plant-protecting agents increased 1.5–1.7 times; the agrochemical load on the environment decreased by 35–60%; and the quality of crop production improved noticeably. The transition to new crop production technologies is justified. Attention is paid to the need to create a domestic physicotechnical and software–hardware basis for precision agriculture, its absence being a major hindrance to the development of “smart agriculture” in Russia.

Exploring educational needs of young farmers in precision agriculture in Serres, Greece, and the perspective of innovative agricultural educational programs

This study attempts to seek the main educational needs of young farmers regarding the use of precision agriculture in Serres, Greece. The main methodological analysis includes a descriptive analysis of frequencies, a categorical principal components analysis (CaPCA) and a categorical regression (CatReg). The most interesting result of this research is the fact that young farmers in Serres are well informed about precision agriculture. On the other hand, the respondents showed an increased level of educational needs and a clear desire to receive further agricultural education related to precision agriculture. Plus, the desired education seems to be influenced by their different social and agricultural characteristics, which can lead to the configuration of suitable educational programs, adapted in the particularities of each category. This ascertainment can lead to creating appropriate educational programs adapted to particularities of each category, in order to confront the multifarious demands related to information and training issues.

Exploring educational needs of young farmers in precision agriculture in Serres, Greece, and the perspective of innovative agricultural educational programs

This study attempts to seek the main educational needs of young farmers regarding the use of precision agriculture in Serres, Greece. The main methodological analysis includes a descriptive analysis of frequencies, a categorical principal components analysis (CaPCA) and a categorical regression (CatReg). The most interesting result of this research is the fact that young farmers in Serres are well informed about precision agriculture. On the other hand, the respondents showed an increased level of educational needs and a clear desire to receive further agricultural education related to precision agriculture. Plus, the desired education seems to be influenced by their different social and agricultural characteristics, which can lead to the configuration of suitable educational programs, adapted in the particularities of each category. This ascertainment can lead to creating appropriate educational programs adapted to particularities of each category, in order to confront the multifarious demands related to information and training issues.

Visible and near infrared spectroscopy coupled to random forest to quantify some soil quality parameters

This study evaluates the use of visible and near infrared spectroscopy (Vis-NIRS) combined with multivariate regression based on random forest to quantify some quality soil parameters. The parameters analyzed were soil cation exchange capacity (CEC), sum of exchange bases (SB), organic matter (OM), clay and sand present in the soils of several regions of Brazil. Current methods for evaluating these parameters are laborious, timely and require various wet analytical methods that are not adequate for use in precision agriculture, where faster and automatic responses are required. The random forest regression models were statistically better than PLS regression models for CEC, OM, clay and sand, demonstrating resistance to overfitting, attenuating the effect of outlier samples and indicating the most important variables for the model. The methodology demonstrates the potential of the Vis-NIR as an alternative for determination of CEC, SB, OM, sand and clay, making possible to develop a fast and automatic analytical procedure.

FINANCIAL ANALYSIS OF THE INVESTMENT IN PRECISION AGRICULTURE TECHNIQUES ON COTTON CROP

ABSTRACT: The aim of this study was to evaluate a financial analysis by the use of precision agriculture (PA) techniques on cotton crop. The experiment was carried out in a cotton field of 91 ha and its result compared to another field with similar area, cultivated with the conventional agricultural techniques. The financial analysis was extrapolated to the total farm area of 3,500 ha. All agricultural inputs applied, during the 2013/14 cotton crop season were analyzed, as well as their costs. The use of precision agriculture techniques over the cotton crop reduces production costs in 6.6%, increases the profitability index and operating profit in 3.3% and 7.9% respectively, when compared to conventional agriculture. The fertilizer application in variable rate, using precision agriculture, provides 41% of costs reduction with these inputs. Profitability and investment analysis indicators demonstrate economic feasibility and return over investment to both production systems (precision agriculture and conventional) over the cotton crop production, however, the use of precision agriculture techniques shows higher economic viability and smaller return over investment time, even having higher initial costs with machines, sensors and maps production.

MAPEAMENTO DO SOLO E OCORRÊNCIA DE PLANTAS DANINHAS E ESTIMATIVAS DO CUSTO DE PRODUÇÃO DE CANA-DE-AÇÚCAR

The use of Precision Agriculture (PA) tools for variable rate herbicide application may contribute to the use of herbicides only in the areas with most weed occurrence, reducing environmental impacts and production costs. The aim of this research was to evaluate the soil apparent electrical conductivity and vegetation indexes, as well as to estimate the economic return of variable rate herbicide application for weed control in the sugarcane culture. The study was conducted in a field of 11 ha planted with sugarcane variety RB855453, which had a large infestation of bermudagrass. Measurements of soil electrical conductivity and NDVI were performed. Based on the maps generated, the area for application of pre- and post-planting herbicides was estimated. The results observed in the ECa maps indicated that the study area could be divided into two sections with different soil textures. NDVI showed the bermudagrass occurrence areas, which represented 18% of the total. The simulation demonstrated that the use of PA could assists in reducing sugarcane production costs by 0.9 % in the stage of soil preparation and planting, 7.2% in the stage of cane-plant and 11.4% in the stage of sugarcane ratoon.

Local adaptation of a national digital soil map for use in precision agriculture

The publicly available Digital Soil Map of Sweden (DSMS) contains topsoil clay content information in a 50 m×50 m grid, and can be used as decision support in precision agriculture. However, it is also common that farmers have undertaken their own soil sampling (one sample per hectare with texture analysed in every third sample). In the present study, such soil samples from 403 farms were used to validate topsoil clay content information derived from 1) DSMS, 2) DSMS locally adapted by residual kriging, 3) DSMS locally adapted by regression kriging and 4) inverse distance weighting interpolation of the soil sample data without using DSMS. The latter has been common practice until now. The best method differed depending on the local accuracy of DSMS, the quality of the soil sampling and the spatial variation structure of the topsoil texture. The ‘Best method’ strategy, which meant to apply all the above methods and choose the one that performed best at each farm, significantly reduced the mean absolute error. We recommend using this strategy to locally adapt regional digital soil maps to derive accurate decision support for use in precision agriculture.

Evaluation of relevant sprayer parameters for use with precision irrigation in landscape

The purpose of this work was to evaluate the capacity of existing landscape irrigation equipment to deliver the expected irrigation depth at a level of uniformity that can potentially be used in a precision irrigation management environment. Popular pop-up sprayer systems from two leading manufacturers (Rain-bird and Hunter variable arc and fixed arc nozzles) were subject to spatial distribution tests, in order to establish the application patterns under ideal conditions. The results indicate that it is not possible to fully rely on the catalog application depth values, with some variable arc nozzles applying up to 200% of the catalog values. These nozzles can have low values of uniformity, with Christiansen´s uniformity coefficient, CU, of between 0.31 and 0.70, making it difficult to obtain good uniformity even under a back-to-back setting. These results indicate that variable arc nozzles are not recommended for use in precision agriculture. It is preferable to use fixed arc nozzles, after actual field evaluation.

Adoption and Use of Precision Agriculture in Brazil: Perception of Growers and Service Dealership

<p>Precision agriculture (PA) is growing considerably in Brazil. However, there is a lack of information regarding to PA adoption and use in the country. This study sought to: (i) investigate the perception of growers and service dealership about PA technologies; (ii) identify constraints to PA adoption; (iii) obtain information that might be useful to motivate producers and agronomists to use PA technologies in the crop production systems. A web-based survey approach method was used to collect data from farmers and services dealership involved with PA in several crop production regions of Brazil. We found that the growth of PA was linked to the agronomic and economic gains observed in the field; however, in some situations, the producers still can not measure the real PA impact in producer system. Economic aspects coupled with the difficulty to use of software and equipment proportioned by the lack of technical training of field teams, may be the main factors limiting the PA expansion in many producing regions of Brazil. Precision agriculture work carried out by dealership in Brazil is quite recent. The most services offered is gridding soil sampling, field mapping for lime and fertilizer application at variable rate. Many producers already have PA equipment loaded on their machines, but little explored, also restricting to fertilizers and lime application. Looking at the currently existing technologies and services offered by dealership, the PA use in Brazil could be better exploited, and therefore, a more rational use of non-renewable resources.</p>

UAS-based soil carbon mapping using VIS-NIR (480–1000 nm) multi-spectral imaging: Potential and limitations

Traditional methods to assess the soil organic carbon (SOC) content based on soil sampling and analysis are time consuming and expensive, and the results are influenced by the sampling design. The aim of this study was to investigate the potential of UAS (Unmanned Aerial Systems) multi-spectral imagery (480–1000nm) for estimating the SOC content in bare cultivated soils at a high spatial resolution (12cm). We performed UAS analysis on the Hoosfield Spring Barley experiment at Rothamsted (UK) where adjacent plots with distinctly different SOC contents, due to different long-term management practices, provide a valuable resource to evaluate this approach. We acquired images (wavelength: 480–550–670–780–880–1000nm) at an altitude of 120m over an area of 2ha using a multi-spectral camera mounted on an UAS. The high-resolution images captured small-scale variations at the soil surface (e.g. shadows, tillage and wheels marks). After a projection in new dimensions by a PCA, we calibrated a support vector machine regression using observations from conventional soil sampling and SOC measurements. The performance of the calibration had a R2 of 0.98 and a RMSE of 0.17%C. A cross-validation showed that the model was robust, with an average R2 of 0.95 and a RMSE of 0.21%. An external validation dataset was used to evaluate the predicted spatial patterns of SOC content and a good fit with an RMSE 0.26%C was obtained. Although this study shows that the methodology has a clear potential for use in precision agriculture or monitoring important soil properties following changes in management, we also identify and discuss its limitations and current shortcomings.

T-MQM: Testbed based Multi-metric Quality Measurement of Sensor Deployment for Precision Agriculture-A Case Study

Efficient sensor deployment is one of the primary requirements of the precision agriculture use case of wireless sensor networks (WSNs) to provide qualitative and optimal coverage and connectivity. The application-based performance variations of the geometrical-model-based sensor deployment patterns restrict the generalization of a specific deployment pattern for all applications. Furthermore, single or double metrics-based evaluation of the deployment patterns focusing on theoretical or simulation aspects can be attributed to the difference in performance of real applications and the reported performance in the literature. In this context, this paper proposes a testbed-based multi-metric quality measurement of sensor deployment for the precision agriculture use case of WSNs. Specifically, seven metrics are derived for the qualitative measurement of sensor deployment patterns for precision agriculture. The seven metrics are quantified for four sensor deployment patterns to measure the quality of coverage and connectivity. Analytical- and simulation-based evaluations of the measurements are validated through testbed experiment-based evaluations which are carried out in “INDRIYA” WSNs testbed. Toward realistic research impact, the investigative evaluation of the geometrical-model-based deployment patterns presented in this paper could be useful for practitioners and researchers in developing performance guaranteed applications for precision agriculture and novel coverage and connectivity models for deployment patterns.

Multispectral analysis of vegetation for remote sensing applications

This paper describes the development of a hardware and software tool for analysis of vegetation cover using remote sensing techniques. Begins with a study of the interaction of electromagnetic waves in the leaves of plants on the single and multiple plate models for the spectrum of visible and infrared, which allows using an application developed in Python found the spectral signature of coverage according to parameters of the leaves (known as an inversion model), which accounts for an approximation of what may occur with the reflectance of the surface of the leaves. The development of a system for acquiring and processing images in the visible and near infrared bands for use in precision agriculture through an unmanned aerial vehicle UAV is also explained.

Sensing systems for precision agriculture in Florida

Many sensing systems have been developed for use in precision agriculture in Florida over the past decade. These systems have been designed for specialty crops such as citrus and blueberry. Systems include those for fruit recognition for yield mapping as well as those for disease detection systems using ground- and aerial-based platforms. Other systems discussed are used in soil phosphorus detection using near-infrared (NIR) and Raman spectroscopy, debris detection generated from citrus mechanical harvesting, detection of citrus fruit dropped on the ground due to disease, citrus leaf nitrogen detection, silage yield mapping, soil nutrients and grain insect detection using NIR spectroscopy. A summary of past efforts is presented in this paper, applications of these different sensing systems are discussed, and future directions are described.

Harnessing coherence of area decomposition and semantic shared spaces for task allocation in a robotic fleet

Task allocation is a fundamental problem in multi-robot systems where heterogeneous robots cooperate to perform a complex mission. A general requirement in a task allocation algorithm is to find an optimal set of robots to execute a certain task. This paper presents the work that harnesses an area decomposition algorithm, and a space-based middleware to facilitate task allocation process in unstructured and dynamic environments. To reduce spatial interference between robots, area decomposition algorithm divides a working area into cells which are then dynamically assigned to robots. In addition, coordination and collaboration among distributed robots are realized through a space-based middleware. For this purpose, the space-based middleware is extended with a semantic model of robot capabilities to improve task selection in terms of flexibility, scalability, and reduced communication overhead during task allocation. In this way a framework which exploits the synergy of area decomposition and semantically enriched space-based approach is created. We conducted performance tests in a specific precision agriculture use case focusing on the utilization of a robotic fleet for weed control introduced in the European Project RHEA – Robot Fleets for Highly Effective Agriculture and Forestry Management.

Delineation of site-specific management zones by fuzzy clustering of soil and topographic attributes: A case study of East Nile Delta, Egypt

The objective of this study was to define site-specific management zones of 67.2 ha of a wheat pivot field at East of Nile Delta, Egypt for use in precision agriculture based on spatial variability of soil and topographic attributes. The field salinity was analysed by reading the apparent soil electrical conductivity (ECa) with the EM38 sensor horizontally and vertically at 432 locations. The field was sampled for soil attributes systematically with a total of 80 sampling location points. All samples were located using GPS hand held unit. Soil sampling for management zones included soil reaction pH, soil saturation percentage, organic matter, calcium carbonates content, available nitrogen, available phosphorus and available potassium. The field topographic attributes were digital elevation model (DEM), slope, profile curvature, plane curvature, compound topographic index (CTI) and power stream index (PSI). The maps of spatial variability of soil and field topographic attributes were generated using ordinary kriging geostatistical method. Principal component analysis (PCA) was used to determine the most important soil and topographic attributes for representing within-field variability. Principal component analysis of input variables indicated that EM38 horizontal readings (EM38h), soil saturation percentage and digital elevation model were more important attributes for defining field management zones. The fuzzy c-means clustering method was used to divide the field into potential management zones, fuzzy performance index (FPI) and normalized classification entropy (NCE) were used to determine the optimal cluster numbers. Measures of cluster performance indicated no advantage of dividing these fields into more than five management zones. The defined management zones not only provided a better description of the soil properties, but also can direct soil sampling design and provide valuable information for site-specific management in precision agriculture.

Optimization of the sampling scheme for maps of physical and chemical properties estimated by kriging

The sampling scheme is essential in the investigation of the spatial variability of soil properties in Soil Science studies. The high costs of sampling schemes optimized with additional sampling points for each physical and chemical soil property, prevent their use in precision agriculture. The purpose of this study was to obtain an optimal sampling scheme for physical and chemical property sets and investigate its effect on the quality of soil sampling. Soil was sampled on a 42-ha area, with 206 geo-referenced points arranged in a regular grid spaced 50 m from each other, in a depth range of 0.00-0.20 m. In order to obtain an optimal sampling scheme for every physical and chemical property, a sample grid, a medium-scale variogram and the extended Spatial Simulated Annealing (SSA) method were used to minimize kriging variance. The optimization procedure was validated by constructing maps of relative improvement comparing the sample configuration before and after the process. A greater concentration of recommended points in specific areas (NW-SE direction) was observed, which also reflects a greater estimate variance at these locations. The addition of optimal samples, for specific regions, increased the accuracy up to 2 % for chemical and 1 % for physical properties. The use of a sample grid and medium-scale variogram, as previous information for the conception of additional sampling schemes, was very promising to determine the locations of these additional points for all physical and chemical soil properties, enhancing the accuracy of kriging estimates of the physical-chemical properties.

Características agronômicas associadas com índices de vegetação medidos por sensores ativos de dossel na cultura da soja

Soybean has always stood out for its economic importance in the national agricultural scenario with an increase in its cultivation throughout the Brazilian territory. With the increasing use of precision agriculture (PA) is of paramount importance to understand the variability of agronomic variables. Currently, spectral sensors in the ground-level have been used to establish relationships between spectral response and crop growth parameters. This is possible through the reading of vegetation indices such as Normalized Difference Vegetation Index using the green band (GNDVI). This study was conducted between October to 2008 and April to 2009, in Ponta Grossa – PR, with the objective of evaluating the spectral response of soybean through GNDVI and its correlation with some agronomic variables, such as: productivity of grain, productivity of dry mass, plant density and plant height. GNDVI readings were taken on six phenological stages using Crop Circle active optical sensor. Regression analyzes were used to determine which agronomic variables established relationship with the GNDVI. The GNDVI in soybean established significant correlations only with grain and MS productivity in EF V9. Thus, to establish the correlation of GNDVI with these variables it is recommended that the readings are carried out in this EF.

Two-step cross correlation–based algorithm for motion estimation applied to fertilizer granules’ motion during centrifugal spreading

Imaging systems are progressing in both accuracy and ro- bustness, and their use in precision agriculture is increasing accordingly. One application of imaging systems is to understand and control the cen- trifugal fertilizing spreading process. Predicting the spreading pattern on the ground relies on an estimation of the trajectories and velocities of ejected granules. The algorithms proposed to date have shown low ac- curacy, with an error rate of a few pixels. But a more accurate estimation of the motion of the granules can be achieved. Our new two-step cross- correlation-based algorithm is based on the technique used in particle image velocimetry (PIV), which has yielded highly accurate results in the field of fluid mechanics. In order to characterize and evaluate our new algorithm, we develop a simulator for fertilizer granule images that ob- tained a high correlation with the real fertilizer images. The results of our tests show a deviation of <0.2 pixels for 90% of estimated velocities. This subpixel accuracy allows for use of a smaller camera sensor, which decreases the acquisition and processing time and also lowers the cost. These advantages make it more feasible to install this system on existing centrifugal spreaders for real-time control and adjustment. C 2011 Society of

Spatial variability in nutritional status of arabic coffee based on dris index

The combined use of precision agriculture and the Diagnosis and Recommendation Integrated System (DRIS) allows the spatial monitoring of coffee nutrient balance to provide more balanced and cost-effective fertilizer recommendations. The objective of this work was to evaluate the spatial variability in the nutritional status of two coffee varieties using the Mean Nutritional Balance Index (NBIm) and its relationship with their respective yields. The experiment was conducted in eastern Minas Gerais in two areas, one planted with variety Catucaí and another with variety Catuaí. The NBIm of the two varieties and their yields were analyzed through geostatistics and, based on the models and parameters of the variograms, were interpolated to obtain their spatial distribution in the studied areas. Variety Catucai, with grater spatial variability, was more nutritional unbalanced than variety Catuai, and consequently produced lower yields. Excess of Fe and Mn makes these elements limiting yield factors.

KRIGAGEM ORDINÁRIA E INVERSO DO QUADRADO DA DISTÂNCIA APLICADOS NA ESPACIALIZAÇÃO DE ATRIBUTOS QUÍMICOS DE UM ARGISSOLO

Um dos fatores que influência a aplicação e utilização da agricultura de precisão é a acurácia com que os mapas de distribuição espacial dos atributos do solo são produzidos no processo de interpolação de dados. O objetivo deste trabalho foi comparar a eficiência da interpolação de dados por meio da krigagem ordinária (Kri) e do inverso do quadrado da distância (IQD) na estimativa de atributos químicos do solo. O experimento foi instalado na parte central de uma encosta, no município de Alegre (ES) sobre um Argissolo Vermelho-Amarelo, com textura argilosa, onde foi construída uma grade de amostragem de solo contendo 128 pontos, espaçados 10 m, para análise dos atributos químicos pH, H+Al e SB. A análise geoestatística mostrou que todos os atributos químicos do solo apresentaram dependência espacial. Notou-se uma maior diferença visual entre os mapas interpolados por Kri e IQD para o atributo SB, apresentando o mapa interpolado por IQD uma maior variabilidade, resultado de uma menor uniformidade dos tons de cinza. Os métodos de interpolação foram eficientes na inferência de dados para os três atributos químicos analisados. Os métodos de interpolação utilizados não apresentaram diferenças significativas, através da análise dos erros da estimativa