Crop Stress Detection with Multispectral Imaging Using Ia - Soto-Gomez, J; Adasme-Soto, P; Zamorano-Illanes, R (Institute of Electrical and Electronics Engineers Inc., 2023)
The article addresses the problem of crop production in the face of biotic and abiotic factors. Highlights the importance of integrated management strategies to optimize resources and increase crop resilience. Multispectral images captured by an Unmanned Aerial Vehicle (UAV) are used to detect stress in crops. The article presents the K-means segmentation method and highlights its superiority over the Otsu threshold method for efficient segmentation. The relevance of vegetation indices and machine learning in stress assessment is emphasized. The proposed methodology includes data preprocessing, crop detection and stress identification, integrating various techniques. Experimental results demonstrate the effectiveness of this implementation in differentiating healthy and stressed crop regions. Taken together, the study contributes to precision agriculture by combining advanced technologies and innovative techniques for more accurate stress detection and more efficient crop management. © 2023 IEEE.
Keywords: multispectral imaging; remote sensing; satellite networks; segmentation; Stress detection