Precision Agriculture and Food Security

Gebbers, Robin; Adamchuk, Viacheslav I.

doi:10.1126/science.1183899

Cited by 1,059 publications

(538 citation statements)

References 8 publications

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“…Precision Agriculture (PA) allows to monitor production, quality and quantity of the agricultural production and also allows the better use of resources, maintaining the quality of the environment and enhancing yield (Gebbers & Adamchuk, 2010). According to Silva et al (2008), the methods and equipment for the adoption of PA are already well known for the cereal crops.…”

Section: Introductionmentioning

confidence: 99%

Management zones in coffee cultivation

Jacintho

Ferraz

Silva

et al. 2017

Rev. bras. eng. agríc. ambient.

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A B S T R A C TThis study aimed to apply precision agriculture techniques in coffee production, using correlation analysis in the definition of management zones. This work was carried out in a 22-ha area of coffee (Coffea arabica L.), cv. 'Topázio MG 1190' , which was sampled on a regular grid, using a topographic GPS, totaling 64 georeferenced samples (on average, 2.9 points per ha). Descriptive analysis was used in the data, followed by Pearson's correlation analysis at 0.05 significance between soil chemical attributes, agronomic characteristics of the plants and altitude. It was possible to verify the correlation of soil chemical attributes, agronomic characteristics of the plants and altitude with coffee yield. Altitude was the variable most correlated with coffee yield through correlation analysis. Therefore, it was chosen as the best variable to define management zones and thematic maps capable to support coffee farmers. Three maps were generated to characterize the area in two, three and four management zones. There was a direct influence on mean yield.Definição de zonas de manejo para cafeicultura R E S U M O Objetivou-se, com o presente estudo, aplicar técnicas de agricultura de precisão no cultivo do café utilizando análise de correlação na definição de zonas de manejo. O trabalho foi desenvolvido em uma área de 22 ha de lavoura de cafeeiro (Coffea arabica L.) da cultivar Topázio MG 1190. Demarcou-se na área em estudo e com a utilização de GPS topográfico, uma malha amostral regular totalizando 64 pontos amostrais georreferenciados (em média 2,9 pontos por ha). Utilizou-se o método de análise descritiva dos dados seguido da análise de correlação de Pearson a 0,05 de significância entre os atributos de solo, características agronômicas da planta e altitude. Foi possível verificar a correlação dos atributos do solo, das características agronômicas das plantas e da altitude com a produtividade. Através da análise de correlação observou-se que a altitude foi a variável que mais se correlacionou com a produtividade sendo, assim, selecionada como variável mais propícia para geração de zonas de manejo e de mapas temáticos capazes de auxiliar os cafeicultores. Foram gerados três mapas que caracterizam a área em duas, três e quatro zonas de manejo. Verificou-se que houve influência direta na média da produtividade.

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Section: Introductionmentioning

confidence: 99%

Management zones in coffee cultivation

Jacintho

Ferraz

Silva

et al. 2017

Rev. bras. eng. agríc. ambient.

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“…There is significant interest in using precision agriculture at the field scale to help growers optimize inputs and production while maximizing profits by developing proper decision support systems [17][18][19]. Typical practices used in precision agriculture include remote sensing, geographical information systems (GIS), spectral imaging, the global positioning system (GPS), data management, and sensors [18,20,21].…”

Section: Introductionmentioning

confidence: 99%

“…Typical practices used in precision agriculture include remote sensing, geographical information systems (GIS), spectral imaging, the global positioning system (GPS), data management, and sensors [18,20,21]. These techniques help to identify patterns of variability within a field to guide soil or plant sampling [22], which then directs optimal and rational spatial input use in conjunction with appropriate equipment (e.g., variable rate input applicators mounted on tractors).…”

Section: Introductionmentioning

confidence: 99%

Challenges in Using Precision Agriculture to Optimize Symbiotic Nitrogen Fixation in Legumes: Progress, Limitations, and Future Improvements Needed in Diagnostic Testing

Thilakarathna

Raizada

2018

Agronomy

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Precision agriculture (PA) has been used for ≥25 years to optimize inputs, maximize profit, and minimize negative environmental impacts. Legumes play an important role in cropping systems, by associating with rhizobia microbes that convert plant-unavailable atmospheric nitrogen into usable nitrogen through symbiotic nitrogen fixation (SNF). However, there can be field-level spatial variability for SNF activity, as well as underlying soil factors that influence SNF (e.g., macro/micronutrients, pH, and rhizobia). There is a need for PA tools that can diagnose spatial variability in SNF activity, as well as the relevant environmental factors that influence SNF. Little information is available in the literature concerning the potential of PA to diagnose/optimize SNF. Here, we critically analyze SNF/soil diagnostic methods that hold promise as PA tools in the short-medium term. We also review the challenges facing additional diagnostics currently used for research, and describe the innovations needed to move them forward as PA tools. Our analysis suggests that the nitrogen difference method, isotope methods, and proximal and remote sensing techniques hold promise for diagnosing field-level variability in SNF. With respect to soil diagnostics, soil sensors and remote sensing techniques for nitrogen, phosphorus, pH, and salinity have short-medium term potential to optimize legume SNF under field conditions.

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“…The basic concept of precision agriculture is defined as "apply the right treatment in the right place at the right time" [5]. The difference in yield and quality is due to differences in soil characteristics and crop growth characteristics by farmland location.…”

Section: Introductionmentioning

confidence: 99%

Orchard Free Space and Center Line Estimation Using Naive Bayesian Classifier for Unmanned Ground Self-Driving Vehicle

et al. 2018

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Abstract:In the case of autonomous orchard navigation, researchers have developed algorithms that utilize features, such as trunks, canopies, and sky in orchards, but there are still various difficulties in recognizing free space for autonomous navigation in a changing agricultural environment. In this study, we applied the Naive Bayesian classification to detect the boundary between the trunk and the ground and propose an algorithm to determine the center line of free space. The naïve Bayesian classification requires a small number of samples for training and a simple training process. In addition, it was able to effectively classify tree trunk's points and noise points of the orchard, which are problematic in vision-based processing, and noise caused by small branches, soil, weeds, and tree shadows on the ground. The performance of the proposed algorithm was investigated using 229 sample images obtained from an image acquisition system with a Complementary Metal Oxide Semiconductor (CMOS) Image Sensor (CIS) camera. The center line detected by the unaided-eye manual decision and the results extracted by the proposed algorithm were compared and analyzed for several parameters. In all compared parameters, extracted center line was more stable than the manual center line results.

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Precision Agriculture and Food Security

Cited by 1,059 publications

References 8 publications

Management zones in coffee cultivation

Management zones in coffee cultivation

Challenges in Using Precision Agriculture to Optimize Symbiotic Nitrogen Fixation in Legumes: Progress, Limitations, and Future Improvements Needed in Diagnostic Testing

Orchard Free Space and Center Line Estimation Using Naive Bayesian Classifier for Unmanned Ground Self-Driving Vehicle

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