9th International Conference on ICT for Sustainable Development

Authors - Deepak Mane, Palaash Padman, Aditya Narsale, Nikita Supekar, Koushal Patil
Abstract - The significance of plants as a primary energy source for humanity cannot be overstated. However, the susceptibility of plant leaves to diseases at any point between sowing and harvesting poses a significant threat to crop production and market value. Detecting these leaf diseases is pivotal in agriculture, but conventional methods demand substantial manpower, time, and an in-depth understanding of plant diseases. In response to these challenges, machine learning along with image processing emerges as a promising solution for disease detection in plant leaves. Here, we proposed customized deep learning model to thoroughly analyze potato plant leaf data, categorizing it into predefined sets. The classification process considers structural features and properties such as color, intensity, and dimensions of the plant leaves. Leveraging machine learning enables a more streamlined and accurate identification of diseases across various plant species. The proposed model offers a comprehensive overview of potato plant diseases and explores customized deep learning classification techniques applied in disease identification. By synthesizing these approaches, the study contributes to the development of automated systems for potato plant disease detection, addressing the limitations of traditional methodologies. This study stands out for its innovative combination of deep learning method with image processing to tackle the complexities of identifying diseases in potato plant leaves. The proposed model improved the overall detection process, offering a more efficient and dependable solution compared to traditional techniques. The proposed model using CNN given an accuracy of 99.6%, generated using 2173 images from the standard dataset.