Hyperspectral Imaging and Machine Learning: A Promising Tool for the Early Detection of Tetranychus urticae Koch Infestation in Cotton

Mariana Yamada (), Leonardo Vinicius Thiesen, Fernando Henrique Iost Filho and Pedro Takao Yamamoto
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Mariana Yamada: Department of Entomology and Acarology, University of São Paulo, Piracicaba 13418-900, Brazil
Leonardo Vinicius Thiesen: Department of Entomology and Acarology, University of São Paulo, Piracicaba 13418-900, Brazil
Fernando Henrique Iost Filho: Department of Entomology and Acarology, University of São Paulo, Piracicaba 13418-900, Brazil
Pedro Takao Yamamoto: Department of Entomology and Acarology, University of São Paulo, Piracicaba 13418-900, Brazil

Agriculture, 2024, vol. 14, issue 9, 1-18

Abstract: Monitoring Tetranychus urticae Koch in cotton crops is challenging due to the vast crop areas and clustered mite attacks, hindering early infestation detection. Hyperspectral imaging offers a solution to such a challenge by capturing detailed spectral information for more accurate pest detection. This study evaluated machine learning models for classifying T. urticae infestation levels in cotton using proximal hyperspectral remote sensing. Leaf reflection data were collected over 21 days, covering various infestation levels: no infestation (0 mites/leaf), low (1–10), medium (11–30), and high (>30). Data were preprocessed, and spectral bands were selected to train six machine learning models, including Random Forest (RF), Principal Component Analysis–Linear Discriminant Analysis (PCA-LDA), Feedforward Neural Network (FNN), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Partial Least Squares (PLS). Our analysis identified 31 out of 281 wavelengths in the near-infrared (NIR) region (817–941 nm) that achieved accuracies between 80% and 100% across 21 assessment days using Random Forest and Feedforward Neural Network models to distinguish infestation levels. The PCA loadings highlighted 907.69 nm as the most significant wavelength for differentiating levels of two-spotted mite infestation. These findings are significant for developing novel monitoring methodologies for T. urticae in cotton, offering insights for early detection, potential cost savings in cotton production, and the validation of the spectral signature of T. urticae damage, thus enabling more efficient monitoring methods.

Keywords: remote sensing; two-spotted spider mite; monitoring; spectral signature (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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