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Obtaining a high accuracy in the classification of plant diseases using digital methods is limited by the diversity of conditions in nature. Previous studies have shown that classification of diseases made with images of lesions caused by diseases is more accurate than a classification made with unprocessed images. This article presents the results obtained when classifying foliar diseases in sunflower using a system composed of a model that automatically segments the leaf lesions, followed by a classification system. The segmentation of the lesions was performed using both Faster R-CNN and Mask R-CNN. For the classification of diseases based on lesions, the residual neural networks ResNet50 and ResNet152 were used. The results show that automatic segmentation of the lesions can be successfully achieved in the case of diseases such as Alternaria and rust, in which the lesions are well-outlined. In more than 90% of the images, at least one affected area has been segmented. Segmentation is more difficult to achieve in the cases of diseases such as powdery mildew, in which the entire leaf acquires a whitish color. Diseased areas could not be segmented in 30% of the images. This study concludes that the use of a system composed of a network that segments lesions, followed by a network that classifies diseases, allows us to both more accurately classify diseases and identify those images for which a precise classification cannot be made.

Recent studies have approached the identification of foliar plant diseases using artificial intelligence, but in these works, classification is achieved using only one side of the leaf. Phytopathology specifies that there are diseases that show similar symptoms on the upper part of the leaf, but different ones on the lower side. An improvement in accuracy can be achieved if the symptoms of both sides of the leaf are considered when classifying plant diseases. In this context, it is necessary to establish whether the captured image represents the leaf on its upper or lower side. From the research conducted using botany books, we can conclude that a useful classification feature is color, because the sun-facing part is greener, while the opposite side is shaded. A second feature is the thickness of the primary and secondary veins. The veins of a leaf are more prominent on the lower side, compared to the upper side. A third feature corresponds to the concave shape of the leaf on its upper part and its convex shape on the lower part. In this study, we aim to achieve upper and lower leaf side classification using both deep learning methods and machine learning models.

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