Estimation of Nitrogen in Rice Crops from UAV-Captured Images
| Autor: | Natalia Cera-Bornacelli, Iván F. Mondragón, Francisco Calderon, Eliel Petro, Julian Colorado, Andres Jaramillo-Botero, David Cuellar, Juan S. Caldas, Maria Camila Rebolledo |
|---|---|
| Přispěvatelé: | Pontificia Universidad Javeriana (PUJ), International Center for Tropical Agriculture [Colombie] (CIAT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), California Institute of Technology (CALTECH), Pontificia universidad Javeriana, Cali, OMICAS program: Optimizacion Multiescala In-silico de Cultivos Agricolas Sostenibles (Infraestructura y validacion en Arroz y Cana de Azucar), The World Bank India, Colombian Ministry of Science, Technology and Innovation, Colombian Ministry of Education, Colombian Ministry of Industry and Turism, ICETEX |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Canopy
010504 meteorology & atmospheric sciences UAV [SDV]Life Sciences [q-bio] Multispectral image 0211 other engineering and technologies Image processing 02 engineering and technology multispectral imagery 01 natural sciences Linear regression plant nitrogen estimation image segmentation 021101 geological & geomatics engineering 0105 earth and related environmental sciences Mathematics Remote sensing 2. Zero hunger Artificial neural network Image segmentation machine learning vegetation index Support vector machine GrabCut General Earth and Planetary Sciences |
| Zdroj: | Remote Sensing Remote Sensing, MDPI, 2020, 12 (20), ⟨10.3390/rs12203396⟩ Remote Sensing; Volume 12; Issue 20; Pages: 3396 |
| ISSN: | 2072-4292 |
| Popis: | Leaf nitrogen (N) directly correlates to chlorophyll production, affecting crop growth and yield. Farmers use soil plant analysis development (SPAD) devices to calculate the amount of chlorophyll present in plants. However, monitoring large-scale crops using SPAD is prohibitively time-consuming and demanding. This paper presents an unmanned aerial vehicle (UAV) solution for estimating leaf N content in rice crops, from multispectral imagery. Our contribution is twofold: (i) a novel trajectory control strategy to reduce the angular wind-induced perturbations that affect image sampling accuracy during UAV flight, and (ii) machine learning models to estimate the canopy N via vegetation indices (VIs) obtained from the aerial imagery. This approach integrates an image processing algorithm using the GrabCut segmentation method with a guided filtering refinement process, to calculate the VIs according to the plots of interest. Three machine learning methods based on multivariable linear regressions (MLR), support vector machines (SVM), and neural networks (NN), were applied and compared through the entire phonological cycle of the crop: vegetative (V), reproductive (R), and ripening (Ri). Correlations were obtained by comparing our methods against an assembled ground-truth of SPAD measurements. The higher N correlations were achieved with NN: 0.98 (V), 0.94 (R), and 0.89 (Ri). We claim that the proposed UAV stabilization control algorithm significantly improves on the N-to-SPAD correlations by minimizing wind perturbations in real-time and reducing the need for offline image corrections. |
| Databáze: | OpenAIRE |
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