Estimation of Podosphaera xanthii in cucumber: machine learning techniques with digital images - Alonso-Barrera, Berenice; Lara Viveros, Francisco Marcelo; Reyes-Rosas, Audberto (Revista Mexicana de Ciencias Agrícolas, 2025)
Phytopathogenic fungi pose a considerable threat to cucurbit crops, so early detection and accurate quantification of diseases are essential to reduce production losses. In this study, a methodology was developed to quantitatively estimate the damage caused by Podosphaera xanthii in cucumber leaves, using digital images and machine learning techniques. Convolutional neural networks were used to visually classify the degree of severity into six predefined categories, using sections of leaves with apparent symptoms of the fungus. Additionally, four supervised classification algorithms were trained and compared: K-NN, decision trees, random forests, and neural networks. The model that obtained the best performance was the random forest model, with an accuracy of 90%, whereas K-NN reached the lowest value (79%). These results position the model as a helpful tool for automated disease monitoring in the field, facilitating phytosanitary decision-making. In addition, the methodology provides a solid foundation for researchers interested in designing and implementing automatic plant disease classification systems, providing clear information on the performance of different classification architectures. The algorithm developed in R allows this solution to be adapted and scaled to different cultivation conditions and types of foliar diseases.