Automatic identification of avocado fruit diseases based on machine learning and chromatic descriptors

Timely identification of phytosanitary problems in agricultural crops is essential to reduce production losses. Artificial intelligence algorithms facilitate their rapid and reliable identification. In this research, three learning classifiers, namely random forest (RF), support vector machine (SVM) and multilayer perceptron (MLP), were evaluated to identify three target classes (healthy fruit, anthracnose [Colletotrichum spp.] and scab [Sphaceloma perseae]) from digital fruit images. Two color descriptor extraction techniques (region selection and image subsampling) were compared with the RF classifier, and an overall classification accuracy (ACC) of 98±0.03 % with region selection and 84±0.08 % with subsampling was obtained. Subsequently, the classifiers were evaluated with color descriptors extracted with region selection. RF and MLP were superior to SVM, with an ACC of 98±0.03 %. Scab and anthracnose were identified with an F1 score of 98 %. %. The high performance of the classifiers shows the potential for applying artificial intelligence paradigms to identify phytosanitary problems in agricultural crops.