Classification of Coffee Biotic Stresses Using Convolutional Neural Networks and Enhanced Image Preprocessing Techniques

Coffee is a big commodity in Brazil, however it’s production is threatened by several biotic stresses. The objective of this paper is the classification of these biotic stresses that attack the leaf, such as leaf miner, rust, cercospora, and brown leaf spot, comparing several methods of image preprocessing to reach a better performance and finding the best one for this task. Using neural networks, part of artificial intelligence, we obtained good results using the networks ResNet50, MobileNetV2 and AlexNet using images with filters such as clahe, gaussian, wavelet and Graythresh. The networks achieved good result, varying from 93% to 98% using certain filters. This paper can aid farmers in the classification of these biotic stresses and can also help future researchers in this area, demonstrating the impact of each filter used in this research and it’s benefits in this type of task.