Enhancing Cassava Disease Detection: Leveraging Deep Convolutional Neural Networks and Data Augmentation for Accurate Diagnosis

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Abstract
Cassava, a widely cultivated staple food crop in the tropics, is frequently afflicted by various diseases that significantly decrease its yield. Cassava leaf disease diagnosis with four common cassava leaf diseases: cassava brown streak disease, cassava green mite, cassava bacterial blight, and cassava mosaic disease using state-of-the-art image classification techniques of deep CNN was implemented. In the proposed method data augmentation approaches have been applied to enlarge the dataset for better model performance. The methodology uses machine learning methods to surpass the conventional manual identification methods for recognizing affected leaf images, potentially increasing agricultural productivity through faster and more accurate disease detection that will lead to improved yield and better resource management in cassava cultivation. Evaluated the performance of various Deep Learning architectures, including LeNet, VGG-16, ResNet-50 and Efficient Net, as well as used various callback techniques. These include early stopping to prevent overfitting and model checkpointing, which saves the best-performing model, along with a learning rate reduction for fine-tuning these models and further optimizing their performance. The research, therefore, opens the possibilities of automated disease detection, greatly helping farmers in timely and accurate diagnosis for better management practices to ensure increased food security in cassava-growing regions, especially when mobile technology increases access to diagnostic tools and supports the field applicability of machine learning models. Further, deploying such models in the field has another advantage in embedding disease surveillance into the day-to-day activities of farmers, which again becomes an effective way to improve crop management strategies and responses in different agricultural contexts.

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