Defoliation Categorization in Soybean with Machine Learning Algorithms and UAV Multispectral Data

Marcelo Araújo Junqueira Ferraz (), Afrânio Gabriel da Silva Godinho Santiago, Adriano Teodoro Bruzi, Nelson Júnior Dias Vilela and Gabriel Araújo e Silva Ferraz
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Marcelo Araújo Junqueira Ferraz: Department of Agriculture, Federal University of Lavras (UFLA), Lavras 37203-202, MG, Brazil
Afrânio Gabriel da Silva Godinho Santiago: Department of Agriculture, Federal University of Lavras (UFLA), Lavras 37203-202, MG, Brazil
Adriano Teodoro Bruzi: Department of Agriculture, Federal University of Lavras (UFLA), Lavras 37203-202, MG, Brazil
Nelson Júnior Dias Vilela: Department of Agriculture, Federal University of Lavras (UFLA), Lavras 37203-202, MG, Brazil
Gabriel Araújo e Silva Ferraz: Department of Agricultural Engineering, Federal University of Lavras (UFLA), Lavras 37203-202, MG, Brazil

Agriculture, 2024, vol. 14, issue 11, 1-13

Abstract: Traditional disease severity monitoring is subjective and inefficient. This study employs a Parrot multispectral sensor mounted on an unmanned aerial vehicle (UAV) to apply machine learning algorithms, such as random forest, for categorizing defoliation levels in R7-stage soybean plants. This research assesses the effectiveness of vegetation indices, spectral bands, and relative vegetation cover as input parameters, demonstrating that machine learning approaches combined with multispectral imagery can provide a more accurate and efficient assessment of Asian soybean rust in commercial soybean fields. The random forest algorithm exhibited satisfactory classification performance when compared to recent studies, achieving accuracy, precision, recall, F1-score, specificity, and AUC values of 0.94, 0.92, 0.92, 0.92, 0.97, and 0.97, respectively. The input variables identified as most important for the classification model were the WDRVI and MPRI indices, the red-edge and NIR bands, and relative vegetation cover, with the highest Gini importance index.

Keywords: Asian rust; random forest; aerial images; multiline (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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