Hybrid Feature Fusion and Deep Learning for High-Accuracy Anthracnose Detection in Chili Plants

Effective disease control and agricultural production require accurate and rapid crop disease detection. The worldwide commodity chili crops are sensitive to several diseases, including anthracnose, which reduces yields and harms farmers. Traditional disease detection approaches are laborious, time-consuming, and require specialized knowledge, adding to intervention delays and economic losses. The lack of systematic chili disease data makes identification more difficult. This research attempts to improve agricultural disease identification utilizing feature fusion, transfer learning, and a Convolutional Neural Network (CNN) to accurately and effectively diagnose chili plant anthracnose disease. Images are represented by two feature extractors: the first is the CNN based on VGG19, and the second is the Hybrid Feature Extractor (HFE). Three feature extraction techniques—Speed Up Robust Feature (SURF), Local Binary Pattern (LBP), and Histogram-Oriented Gradient (HOG) are combined into a single fused feature vector by the HFE. The classification model is then created by combining these two feature vectors. Using this combined feature set, a CNN with a fully connected layer and SoftMax function is trained to identify whether chili images are healthy or unhealthy. The model is also improved and optimized through data augmentation. The feature fusion approach shows great promise because it can more precisely detect anthracnose disease in chilli plants. Using 128 x 128 pixel images, the model learned at a rate of 0.01 and achieved 99.58% success after 100 iterations. Regardless of different batch sizes and learning rates, the model performs well. When compared to the top models currently in use, the feature fusion approach produces better performance results. The financial loss caused by anthracnose disease and the research on managing chili crops will benefit sustainable agriculture.