Identification of management zones in precision agriculture: An evaluation of alternative cluster analysis methods

Gavioli, Alan; Souza, Eduardo Godoy de; Bazzi, Claudio; Schenatto, Kelyn; Betzek, Nelson Miguel

doi:10.1016/j.biosystemseng.2019.02.019

Cited by 71 publications

(77 citation statements)

References 34 publications

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“…The geometric center of each maize core is considered as the localization point. By comparing the accuracy rates and segmentation times of six common algorithms(i.e., (i) the continuous max-flow algorithm, (ii) minimum cross entropy, (iii) ISODATA, (iv) Otsu, (v) k-means, and (vi) fuzzy thresholding segmentation) [25][26][27][28][29][30][31], we select the three with the best segmentation results (i.e., minimum cross entropy, ISODATA, and the Otsu algorithm) and use them to recognize the positions of maize cores based on four core brightness indexes (i.e., gray, Y, vHSV, and extragreen) [21,[33][34][35] (12 combined strategies in total). After experimental validation with many field images, the core localization effects of 12 combined strategies are compared.…”

Section: Design Of Image Recognition Systemmentioning

confidence: 99%

“…According to the literature, there are multiple approaches to image segmentation. However, the common methods used for maize segmentation [20][21][22][23][24][25][26][27][28][29][30][31] have not yet been systematically compared with respect to their ability to recognize maize cores. To guide real-world applications of localization and fertilization for maize, this study collected a large number of images of maize at the seedling stage, under different weather and field conditions (i.e., images with more weeds on sunny days (MS), images with fewer weeds on sunny days (FS), images with more weeds on cloudy days (MC), and images with fewer weeds on cloudy days(FC)).…”

Section: Introductionmentioning

confidence: 99%

“…To guide real-world applications of localization and fertilization for maize, this study collected a large number of images of maize at the seedling stage, under different weather and field conditions (i.e., images with more weeds on sunny days (MS), images with fewer weeds on sunny days (FS), images with more weeds on cloudy days (MC), and images with fewer weeds on cloudy days(FC)). In this study, we first apply a minimum cross entropy model to recognize the maize cores, and we systematically compare different image segmentation algorithms [25][26][27][28][29][30][31] with respect to accuracy and time efficiency when recognizing the maize cores. We find that the minimum cross entropy model based on the gray level has a better performance, and we recommend using such a model in the real-time localization of maize cores under complex field environments.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Localization Approach for Maize Cores at Seedling Stage Based on Machine Vision

2020

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To realize quick localization of plant maize, a new real-time localization approach is proposed for maize cores at the seedling stage, which can meet the basic demands for localization and quantitative fertilization in precision agriculture and reduce environmental pollution and the use of chemical fertilizers. In the first stage, by taking pictures of maize at the seedling stage in a field with a monocular camera, the maize is segmented from the weed background of the picture. And then the three most-effective methods (i.e., minimum cross entropy, ISODATA, and the Otsu algorithm) are found from six common segmentation algorithms after comparing the accuracy rate of extracting maize and the time efficiency of segmentation. In the second stage, plant core from segmented maize image is recognized, and localized, based on different brightness of the rest part of maize core and plant. Then the geometric center of maize core is considered as localization point. the best effect of extracting maize core was found from the minimum cross entropy method based on gray level. According to experimental validation using many field pictures, under weedy conditions on sunny days, the proposed method has a minimum recognition rate of 88.37% for maize cores and is more robust at excluding weeds.

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Section: Design Of Image Recognition Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real-Time Localization Approach for Maize Cores at Seedling Stage Based on Machine Vision

2020

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“…Furthermore, remote sensing can be used for precision agriculture to discern the "health status" of crops [9] in a measurement system net located in scattered fields. Advances in precision agriculture have allowed for prompt, site-specific management of the limiting factors of the production system, by which decisions to apply resources and agronomic practices are dictated in progress by the variability detected in the field among the crops [10]. Site-specific management means that inputs, namely, irrigation water, fertilizers, and pesticides, are applied only where and when necessary to maximize the desired result, which in most cases is linked to income maximization and reduced environmental impact [11].…”

Section: Introductionmentioning

confidence: 99%

Comparison of UAV Photogrammetry and 3D Modeling Techniques with Other Currently Used Methods for Estimation of the Tree Row Volume of a Super-High-Density Olive Orchard

et al. 2019

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A comparison of three different methods to evaluate the tree row volume (TRV) of a super-high-density olive orchard is presented in this article. The purpose was to validate the suitability of unmanned aerial vehicle (UAV) photogrammetry and 3D modeling techniques with respect to manual and traditional methods of TRV detection. The use of UAV photogrammetry can reduce the amount of estimated biomass and, therefore, reduce the volume of pesticides to be used in the field by means of more accurate prescription maps. The presented comparison of methodologies was performed on an adult super-high-density olive orchard, planted with a density of 1660 trees per hectare. The first method (TRV1) was based on close-range photogrammetry from UAVs, the second (TRV2) was based on manual in situ measurements, and the third (TRV3) was based on a formula from the literature. The comparisons of TRV2-TRV1 and TRV3-TRV1 showed an average value of the difference equal to +13% (max: +65%; min: −11%) and +24% (max: +58%; min: +5%), respectively. The results show that the TRV1 method has high accuracy in predicting TRV with minor working time expenditure, and the only limitation is that professionally skilled personnel is required.

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“…Salt content in soils is dependent on the physical-hydric characteristics, and consequently requires management by zones, established through mapping (ALARCÓN-JIMÉNEZ; CAMACHO-TAMAYO; BERNAL, 2015). Hence, it is possible to qualitatively identify areas with higher susceptibility to salinization based on the soil physical characteristics and, therefore, allowing application of different management methods with higher precision (GAVIOLI et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Space Dependence of Soil Moisture and Soil Electrical Conductivity in Aluvial Region1

Lopes

Montenegro

2019

R_I

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SPACE DEPENDENCE OF SOIL MOISTURE AND SOIL ELECTRICAL CONDUCTIVITY IN ALUVIAL REGION1 IUG LOPES2 E ABELARDO ANTONIO DE ASSUNÇÃO MONTENEGRO3 1Paper extracted from the doctoral thesis of the first author. 2Department of Agronomy, Instituto Federal de Educação, Ciência e Tecnologia Baiano, BR 349, Km 14 - Zona Rural, CEP: 47600-000, Bom Jesus da Lapa - BA, Brazil; iug.lopes@ifbaiano.edu.br - ORCID: 0000-0003-0592-4774. 3Department of Agricultural Engineering, Universidade Federal Rural de Pernambuco, Rua Dom Manoel de Medeiros, Dois Irmão, CEP: 52171-900, Recife - PE, Brazil; abelardo.montenegro@ufrpe.br -ORCID: 0000-0002-5746-8574. 1 ABSTRACT Spatial information on soil characteristics is essential to proper decision-making regarding to the environment and land use management. The objective of this work was the investigation of cross - variance between soil moisture and apparent soil electrical conductivity (CEa), under different land uses in an alluvial valley of Pernambuco. The study was developed at the Advanced Research Unit of Universidade Federal Rural de Pernambuco (UFRPE), located at Brígida River Basin, municipality of Panamirim-PE. Soil samples were collected in a regular mesh of 20 x 10 m, for soil moisture by gravimetric method and, following a regular 10 x 10 m mesh, CEa measurements were performed using EM38® device. Cross-semivariograms were assessed and spatial dependence was verified by geostatistical procedures. It was verified in geostatistical procedures low variation for soil moisture and intermediate variation for CEa. The use of geostatistics allowed identification of covariance between soil moisture and ECa, as well as spatial dependence for both variables, for agricultural areas. It was verified that soil moisture, even at levels close to residual, constitutes a relevant secondary component for increasing soil salinity maps precision, and hence to precision agriculture. Keywords: geostatistics, semi-arid, precision agriculture LOPES, I. E MONTENEGRO, A. A. DE A. DEPENDÊNCIA ESPACIAL DA UMIDADE DO SOLO E CONDUTIVIDADE ELÉTRICA EM REGIÃO ALUVIAL 2 RESUMO Informações espaciais sobre as características do solo são essenciais para uma tomada de decisão adequada em relação ao meio ambiente e ao gerenciamento do uso do solo. O objetivo deste trabalho foi investigar a variância cruzada entre a umidade do solo e a condutividade elétrica aparente do solo (CEa), sob diferentes usos do solo em um vale aluvial de Pernambuco. O estudo foi desenvolvido na Unidade de Pesquisa Avançada da Universidade Federal Rural de Pernambuco (UFRPE), localizada na bacia do rio Brígida, município de Panamirim-PE. As amostras de solo foram coletadas em uma malha regular de 20 x 10 m, para a umidade do solo pelo método gravimétrico e, seguindo uma malha regular de 10 x 10 m, as medidas de CEa foram realizadas usando o dispositivo EM38®. Os semivariogramas cruzados foram avaliados e a dependência espacial foi verificada por procedimentos geoestatísticos. Verificou-se procedimentos geoestatísticos, uma baixa variação da umidade do solo e variação intermediária para CEa. O uso da geoestatística permitiu identificar a covariância entre a umidade do solo e o CEa, bem como a dependência espacial para ambas as variáveis, para as áreas agrícolas. Verificou-se que a umidade do solo, mesmo em níveis próximos ao residual, constitui um componente secundário relevante para o aumento da precisão do mapeamento da salinidade do solo e, consequentemente, para a agricultura de precisão. Palavras-chave: geoestatística, semiárido, agricultura de precisão

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Identification of management zones in precision agriculture: An evaluation of alternative cluster analysis methods

Cited by 71 publications

References 34 publications

Real-Time Localization Approach for Maize Cores at Seedling Stage Based on Machine Vision

Real-Time Localization Approach for Maize Cores at Seedling Stage Based on Machine Vision

Comparison of UAV Photogrammetry and 3D Modeling Techniques with Other Currently Used Methods for Estimation of the Tree Row Volume of a Super-High-Density Olive Orchard

Space Dependence of Soil Moisture and Soil Electrical Conductivity in Aluvial Region1

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