Diagnostic study of nitrogen nutrition in cotton based on unmanned aerial vehicle RGB images

Lu WANG; Qiushuang YAO; Ze ZHANG; Shizhe QIN; Hongyu WANG; Feng XU; Mi YANG; Xin LV; Lulu MA

doi:10.15835/nbha52213728

Authors

Lu WANG Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
Qiushuang YAO Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
Ze ZHANG Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
Shizhe QIN Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
Hongyu WANG Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
Feng XU Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
Mi YANG Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
Xin LV Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)
Lulu MA Shihezi University College of Agriculture/ The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi 832003 (CN)

DOI:

https://doi.org/10.15835/nbha52213728

Keywords:

composite score, image features, machine learning, nitrogen nutrition index

Abstract

Nitrogen fertilizer levels significantly affect crop growth and development, necessitating precision management. Most studies focus on nitrogen nutrient estimation using vegetation indices and textural features, overlooking the diagnostic potential of color features. Hence, we investigated cotton nitrogen nutrition status using unmanned aerial vehicle (UAV) image features and the nitrogen nutrient index (NNI). Random frog algorithm - and random forest-screened image feature sets significantly correlated with the NNI, which were substituted into four machine learning algorithms for NNI estimation modeling. The composite scores (F) of optimal image feature sets were calculated using the coefficient of variation method for comprehensive cotton nitrogen nutrient diagnosis. Validation of the model for determining the critical nitrogen concentration in cotton yielded a coefficient of determination R² = 0.89, root mean square error RMSE = 0.50 g (100 g)^-1, and mean absolute error MAE = 0.44, demonstrating improved performance. Additionally, our novel NNI estimation model constructed based on the optimal image feature sets exhibited R²_c = 0.97, RMSE_c = 0.02, MAE_c = 0.02, R²_v = 0.85, RMSE_v = 0.05, and MAE_v = 0.04. Polynomial fitting of the composite index with NNI indicated that the model was reliable and yielded the following diagnostic criterion: 0.48 < F2 < 0.67 indicated nitrogen overapplication, whereas F2 < 0.48 or F2 > 0.67 indicated nitrogen deficiency. This study demonstrates the superior effectiveness of using UAV RGB image feature sets for NNI estimation and the quick, accurate diagnosis of cotton nitrogen levels, which will help guide nitrogen fertilizer application.

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