Boosting Rice Crop Management: Disease Detection by Leveraging Transfer Learning in CNNs

This project introduces a novel Rice Leaf Disease Detection System utilizing Convolutional Neural Networks (CNN) and Transfer Learning. The objective is to mitigate the risks that diverse diseases, including the most prominent and frequently occuring diseases bring to rice crops and their potential to drasti- cally reduce productivity. For efficient crop management and the security of the world's food supply, timely and precise detection is essential. The dataset for training and validation comprises high-quality rice leaf images, ensuring robust model performance throughout a range of disease instances and environmental circumstances. The architecture of CNN is tailored to obtain intricate information from illustrations of rice leaves,enabling accurate differentiation between healthy and diseased leaves. Transfer learning techniques fine-tune pre-trained CNN models to optimize detection performance, minimizing the need for exten- sive computational resources. The remarkable precision attained in recognising and categorising rice leaf diseases through the utilisation of CNNs suggests the auspicious possibilities of deep learning models within the realm of plant disease identification. Farmers in areas where rice is grown will find special value in this research as it provides a useful tool for quickly and accu- rately identifying illnesses, facilitating timely decision-making to safeguard agricultural yields and quality in real-time agricultural systems.