Comparing Regression and Classification Models to Estimate Leaf Spot Disease in Peanut (Arachis hypogaea L.) for Implementation in Breeding Selection

Late leaf spot (LLS) is an important disease of peanut, causing global yield losses. Develop-ing resistant varieties through breeding is crucial for yield stability, especially for smallholder farmers.However, traditional phenotyping methods used for resistance selection are laborious and subjective.Remote sensing offers an accurate, objective, and efficient alternative for phenotyping for resistance.The objectives of this study were to compare between regression and classification for breeding,and to identify the best models and indices to be used for selection. We evaluated 223 genotypesin three environments: Serere in 2020, and Nakabango and Nyankpala in 2021. Phenotypic datawere collected using visual scores and two handheld sensors: a red–green–blue (RGB) camera andGreenSeeker. RGB indices derived from the images, along with the normalized difference vegetationindex (NDVI), were used to model LLS resistance using statistical and machine learning methods.Both regression and classification methods were also evaluated for selection. Random Forest (RF),the artificial neural network (ANN), and k-nearest neighbors (KNNs) were the top-performing algo-rithms for both regression and classification. The ANN (R2: 0.81, RMSE: 22%) was the best regressionalgorithm, while the RF was the best classification algorithm for both binary (90%) and multiclass(78% and 73% accuracy) classification. The classification accuracy of the models decreased with theincrease in classification classes. NDVI, crop senescence index (CSI), hue, and greenness index werestrongly associated with LLS and useful for selection. Our study demonstrates that the integration ofremote sensing and machine learning can enhance selection for LLS-resistant genotypes, aiding plantbreeders in managing large populations effectively
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