Jul 2026· ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences· Vol XI-3-2026, pp. 757-764· 0 citations· 2 references
TL;DR
This study evaluated the performance of multispectral and hyperspectral data in estimating coffee crop productivity using linear regression, SVM, and neural networks to highlight the negative correlation pattern typically observed in perennial crops.
Abstract
Abstract. This study evaluated the performance of multispectral (Mavic 3M) and hyperspectral (Blue Wave) data in estimating coffee crop productivity using linear regression, SVM, and neural networks. Forty plots with different varieties were analyzed. Multispectral data showed high correlation with productivity, especially the Red Edge (r = 0.704) and Green (r = 0.644) bands. For hyperspectral data, PRI (r = 0.535), GNDVI (r = -0.394), NDVI (r = -0.33), and CIRE (r = -0.328) were significant, highlighting the negative correlation pattern typically observed in perennial crops. Neural network models applied to hyperspectral data achieved the best performance (r = 0.92; RMSE = 6.6%), surpassing multispectral models (r = 0.84; RMSE = 9.4%).
Accurate estimation of leaf nitrogen content is essential for optimizing fertilization management and improving crop productivity. This study proposes a nondestructive hyperspectral imaging framework combined with advanced machine learning algorithms to classify nitrogen levels in tomato leaves (Solanum lycopersicu...
Mohammad Vahedi Torshizi, S. Sabzi, Mohsen Azadbakht et al.· Scientific Reports· 0 citations
Wheat gluten index is an important indicator of food quality inspection. Although hyperspectral technology offers non-destructive and rapid detection potential, its application faces challenges including high-dimensional data redundancy, multicollinearity among adjacent wavelengths, and model overfitting risks in small...
Shi-You Zhu, Juan Bai, Yu-Long Chen et al.· Food Chemistry· 0 citations
It is suggested that three-dimensional spectral information has strong capacity for characterizing potato PNC, and that combining multiple types of spectral features can further improve estimation accuracy.
Ming Jin, Juan-li Ju, Si-Qi Li et al.· Potato Research· 0 citations
It is demonstrated that hyperspectral sensing provides a valuable reference for developing accurate disease detection models, whereas UAV multispectral imagery represents a practical and scalable solution for operational precision agriculture.
Andrzej Wójtowicz, Jan Piekarczyk, Marek Wójtowicz et al.· Remote Sensing· 0 citations
Machine learning combined with multispectral remote sensing provides an efficient and non-destructive approach for monitoring fruit tree nutrient status. However, conventional machine learning methods have limited ability to capture complex spectral-feature interactions, while deep neural networks often suffer from ove...
Fei Jia, Zhi-Cheng Jia, Bo-Lin Wang et al.· Agronomy· 0 citations
A framework for leaf-level nitrogen estimation using full-range visible, near-infrared, and short-wave infrared hyperspectral reflectance data combined with advanced ML models and band selection (BS) strategies is proposed.
E. Koutsovili, Giovanni Lombardo, Salvatore Serrano et al.· IEEE Journal of Selected Top...· 0 citations
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