Sugarcane Disease Detection Using Deep-Learning and LIME

Crop diseases pose several challenges in the agricultural industry. Plant diseases can have a devastating impact on both yield and quality loss. This project presents a deep-learning based sugarcane disease classification and alert system to facilitate machine detection of disease, as well as actions to take in response to diagnosis. A dataset of images of sugarcane leaves, was modified through advanced pre-processing techniques such as cropping, rotating, image enhancement, detections edges, and adjusting for wavy images. The techniques used for data augmentation helped address class imbalance as well as overfitting. The classification model implemented for this project is the EfficientNet architectures to produce better feature extraction and predictions. The classification model was trained on the pre-processed and augmented dataset and reached 97 percent accuracy on the test data after 150 epochs. The explainability of the classifying model was provided through LIME (Local Interpretable Model-Agnostic Explanations), which allowed users to see the explanation for classification for an individual image. The smart classifying system was developed as a web application (UI) and allows users to input their image to be diagnosed. Once diagnosed, the model classifies the disease and detail actions, including: recommended treatments and precautionary actions. This system shows the power of artificial intelligence to enhance management of diseases in agriculture, while providing a scalable user-friendly environment to help faciliate on farm decision-making regarding crop health and yield.