A Low Computational Cost Deep Learning Approach for Localization and Classification of Diseases and Pests in Coffee Leaves

Coffee cultivation is of extreme economic importance in many regions of the world, but productivity is hampered by the various diseases and pests that affect the leaves of the plants, damaging both the quality and yield of the harvest. In this context, deep learning presents itself as a promising solution for the automatic identification of plant diseases, reducing dependence on human inspection and increasing efficiency in crop management. In this sense, this study proposes a novel two-stage approach, detecting the diseased region of the coffee leaf and classifying the diseases into Miner, Rust, Cercospora and Phoma on coffee leaves. A new dataset, derived from the BRACOL and Diseases and Pests in Coffee Leaves datasets, was created and used to improve class balance and robustness. In the first stage, the YOLOv8 model is being used to detect the diseased regions. For the second stage, the InceptionResNetv2, DenseNet169, Resnet50 and ShuffleNet models are being trained and used to classify the detected region, and a modification to a low computational cost classification architecture called SmallPavicNet-MC is being proposed. The results obtained are compared and the performance analysis of the detection models shows that YOLOv8 obtained the best performance with a mAP (Mean Average Precision) of 85.1% and for classification the DenseNet169 model obtained the highest average accuracy with 97.93%. The SmallPavicNet-MC model presents itself as the best alternative with reduced complexity and an accuracy of 97.77%. The combination of promising performance and reduced computational cost suggests that SmallPavicNet-MC can be integrated into plantation monitoring systems, contributing to more effective management of diseases and pests affecting coffee production.