Deep Learning-Driven Computational Intelligence for Leaf Disease Identification for Smart Agriculture

In the context of smart farming, Artificial Intelligence (AI) plays a crucial role in enhancing agricultural practices, from soil management to crop yield optimization. By leveraging deep learning and image processing techniques, AI can address various challenges across different agricultural sectors, particularly in detecting and mitigating plant diseases. This paper focuses on the use of these technologies for leaf disease detection to minimize crop loss. Deep learning and image processing are key components in identifying, classifying, and monitoring plant diseases. The study reviews various types of leaf diseases and examines the effectiveness of existing algorithms for detection. To improve performance, an algorithm based on ensemble learning methods is proposed. Experimental investigation is conducted on tomato leaf diseases, followed by a comparative analysis of the results. The proposed AI-driven system enables early disease detection, facilitating timely monitoring and intervention, ultimately reducing crop loss and improving overall crop health through proactive management and precautionary measures.