Published Paper Details:

In the context of fostering a robust agricultural economy, particularly in nations like India, the early detection of plant leaf diseases stands as a pivotal requirement. Beyond its implications for agricultural productivity, the significance of timely disease detection extends to ensuring the nutritional integrity of a sizable population. This survey paper proposes a comprehensive exploration of methodologies for the early identification of tomato plant leaf diseases, emphasizing the integration of advanced image processing techniques, convolutional neural networks (CNNs), and open-source algorithms. By synthesizing and evaluating existing research in this domain, the paper aims to provide an in-depth understanding of the state-of-the-art technologies, their strengths, limitations, and potential areas for improvement. The culmination of this survey contributes to the development of a dependable, secure, and precise framework tailored to the specificities of tomato plant diseases. The insights derived from this survey are poised to inform and guide future research endeavors, offering a holistic perspective on the advancements in early disease detection and predictive mechanisms within the realm of agricultural practices.