Transforming Agriculture with AI, collaborative solutions and Image based diagnosis

Abstract: Our project endeavors to create an all-encompassing platform that not only diagnoses plant diseases but also provides solutions for limited market access and social problems faced by farmers. By integrating advanced algorithms and leveraging data analytics, we seek to offer personalized recommendations and tailored support to farmers, thereby enhancing their productivity and profitability. Additionally, our platform promotes knowledge exchange and collaboration among stakeholders, fostering a culture of innovation and continuous improvement in agricultural practices. Through strategic partnerships and community-driven initiatives, we aim to address systemic inequalities, promote sustainable agricultural development, and create a more equitable and resilient farming ecosystem.

Keywords: ResNet, Image classification, Convolutional neural network (CNN), Data augmentation, One Cycle learning rate scheduling, Cross-entropy loss, Batch normalization, Maxpooling.