Joint measurements of NDVI and crop production data-set related to combination of management zones delineation and nitrogen fertilisation levels

Cordero, Eleonora; Longchamps, Louis; Khosla, Raj; Sacco, Dario

doi:10.1016/j.dib.2019.104968

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…From the computational point of view, kmeans-nc-dec is also optimal, since excluding coordinates decreases the data dimension, while the dimension is further reduced after feature selection. It is worth noting that since some of the soil attributes are spatially correlated (e.g., Ca and CEC), including spatial data in clustering is generally recommended, as they were explicitly considered in similar applications [9,25,[27][28][29][30]. Since we used the raster data after spatial interpolation using kriging, the spatial correlation has been taken into consideration once, so that reconsidering it one more time means overweighing the spatial information.…”

Section: Evaluation Of Clustering Algorithmsmentioning

confidence: 99%

“…Most of the papers concerning MZ delineation have not explicitly discussed the inclusion of the geographical coordinates of the data. However, the coordinates data were explicitly used in [9,25,[27][28][29][30]. However, so far, there is no consensus not only on the clustering method but also the format of input data and inclusion or exclusion of spatial correlation and locations.…”

Section: Introductionmentioning

confidence: 99%

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Clustering and Smoothing Pipeline for Management Zone Delineation Using Proximal and Remote Sensing

Javadi

Guerrero

Mouazen

2022

Sensors

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In precision agriculture (PA) practices, the accurate delineation of management zones (MZs), with each zone having similar characteristics, is essential for map-based variable rate application of farming inputs. However, there is no consensus on an optimal clustering algorithm and the input data format. In this paper, we evaluated the performances of five clustering algorithms including k-means, fuzzy C-means (FCM), hierarchical, mean shift, and density-based spatial clustering of applications with noise (DBSCAN) in different scenarios and assessed the impacts of input data format and feature selection on MZ delineation quality. We used key soil fertility attributes (moisture content (MC), organic carbon (OC), calcium (Ca), cation exchange capacity (CEC), exchangeable potassium (K), magnesium (Mg), sodium (Na), exchangeable phosphorous (P), and pH) collected with an online visible and near-infrared (vis-NIR) spectrometer along with Sentinel2 and yield data of five commercial fields in Belgium. We demonstrated that k-means is the optimal clustering method for MZ delineation, and the input data should be normalized (range normalization). Feature selection was also shown to be positively effective. Furthermore, we proposed an algorithm based on DBSCAN for smoothing the MZs maps to allow smooth actuating during variable rate application by agricultural machinery. Finally, the whole process of MZ delineation was integrated in a clustering and smoothing pipeline (CaSP), which automatically performs the following steps sequentially: (1) range normalization, (2) feature selection based on cross-correlation analysis, (3) k-means clustering, and (4) smoothing. It is recommended to adopt the developed platform for automatic MZ delineation for variable rate applications of farming inputs.

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Section: Evaluation Of Clustering Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Clustering and Smoothing Pipeline for Management Zone Delineation Using Proximal and Remote Sensing

Javadi

Guerrero

Mouazen

2022

Sensors

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“…The delimitation of MZs supports the management of spatial variability [8] and allows for greater control of cause-and-effect relationships, e.g., to evaluate the increase in yield potential under specific conditions [9][10][11][12][13][14][15]. MZs can help mitigate the unmanageable external effects and optimize the use of inputs, such as seeding, which could have optimal ranges for a determined zone.…”

Section: Introductionmentioning

confidence: 96%

Definition of Optimal Maize Seeding Rates Based on the Potential Yield of Management Zones

et al. 2021

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The decision on crop population density should be a function of biotic and abiotic field parameters and optimize the site-specific yield potential, which can be a real challenge for farmers. The objective of this study was to investigate the yield of maize hybrids subjected to variable rate seeding (VRS) and in differentiated management zones (MZs). The experiment was conducted between 2013 and 2015 in a commercial field in the Central-West region of Brazil. First, MZ were delineated using the K-means algorithm with layers involving soil electrical conductivity, yield maps from previous years, and elevation. Seven maize hybrids at five seeding rates were evaluated in the context of each MZ and the cause-and-effect relationship with soil attributes was investigated. Optimal yields were obtained for crop population densities between 70,000 plants ha−1 and 80,000 plants ha−1. Hybrids which perform well under higher densities are key in achieving positive results using VRS. The plant population densities that resulted in maximum yields were obtained for densities at least 27% higher than the recommended seeding rates. The yield variance between MZs can be explained by the variance in soil attributes, while the yield variance within MZs can be explained by the variance in plant population densities. The study shows that on-farm experimentation can be key for obtaining information concerning yield potential. The management by VRS in different MZs is a low-cost technique that can reduce input application costs and optimize yield according to the site-specific potential of the field.

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“…Las cámaras multiespectrales permiten el reconocimiento de diferentes plantas y objetos dependiendo de la firma espectral que éstos generen, todo esto gracias a la fácil detección de cinco bandas espectrales e imágenes RGB, estás bandas hacen parte del espectro no visible, es decir, que es imperceptible a nuestros ojos; por ende, en proyectos como el de reconocimiento de plantas de algodón en grupos o individualmente con el objetivo de detectar posibles ubicaciones que puedan proporcionar un hábitat para los gorgojos del algodonero [11] y en la discriminación de variedades de plantas de té utilizando el mismo tipo de sistema de imágenes y análisis de múltiples características [12], se puede apreciar la capacidad y el alcance que tiene esta tecnología no invasiva. La utilización de redes neuronales convolucional ha traído consigo avances en la investigación de reconstrucción de imágenes multiespectrales [13]. Centrado en gran medida en la reconstrucción de MSI utilizando los canales R-G-B del MSI como entradas del modelo para la posterior de recopilación de información y análisis [14].…”

Section: Introductionunclassified

Optimización de la evaluación de pastos mediante la implementación de imágenes multiespectrales y vehículo aéreo no tripulado

Pelaez Carrillo,

Gualdron Guerrero,

Torres Chavez

2024

RCTA

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Este artículo desarrolla una forma de optimizar la evaluación de pastos utilizando vehículos aéreos no tripulados (UAV) y el análisis de imágenes multiespectrales. La investigación se llevó a cabo en el Municipio de Pamplona, Colombia, con el objetivo de comprender y documentar el crecimiento y evolución del pasto en áreas agrícolas. La metodología utilizada en la investigación incluyó la zonificación del terreno para identificar condiciones favorables para el estudio, de tal manera que se pudiera garantizar que existieran áreas adecuadas para observar el desarrollo del pasto y facilitar el acceso a los instrumentos experimentales. Se destacan aspectos importantes como la obtención de puntos GPS en tierra con el fin de crear polígonos que se disponen como el área de estudio y que permitirán la planificación de vuelos de vehículos aéreos no tripulados (UAV), que dan paso al uso de software de gestión de vuelos autónomos. La adquisición de imágenes multiespectrales se da gracias al uso de cámaras multiespectral incorporadas en el UAV, capaces de registrar información en múltiples bandas espectrales dentro y fuera del espectro visible, como el infrarrojo cercano y el borde de rojo. El análisis estadístico proporcionó una visión detallada de las condiciones agrícolas al revelar correlaciones significativas entre el Índice de Vegetación Normalizado (NDVI) y varios parámetros del suelo como el potasio (K) y el fosforo (P). Este método innovador proporciona datos y visualizaciones precisos que ayudan a tomar decisiones sobre la gestión sostenible de pastos en la región.

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Joint measurements of NDVI and crop production data-set related to combination of management zones delineation and nitrogen fertilisation levels

Cited by 4 publications

References 2 publications

Clustering and Smoothing Pipeline for Management Zone Delineation Using Proximal and Remote Sensing

Clustering and Smoothing Pipeline for Management Zone Delineation Using Proximal and Remote Sensing

Definition of Optimal Maize Seeding Rates Based on the Potential Yield of Management Zones

Optimización de la evaluación de pastos mediante la implementación de imágenes multiespectrales y vehículo aéreo no tripulado

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