AgriScan: Next-Gen Corn Leaf Disease Detection and Analysis Using Deep Learning Models

The relentless advancement of computer science, with a focal point on Artificial Intelligence (AI), has ushered in a new era of innovation, presenting solutions to multifaceted challenges across various domains. This report delves into the transformative potential of AI, specifically leveraging deep learning techniques, to tackle a pressing issue faced by farmers during the growth and production stages of corn—accurate diagnosis of crop diseases. Conventional methods have proven inadequate, resulting in substantial production losses. This comprehensive solution is poised to empower farmers by facilitating remote monitoring and enabling the timely detection of three prevalent corn leaf diseases: leaf blight, leaf gray spot, and rust. The heart of our solution lies in the development of a sophisticated deep learning model meticulously trained on extensive datasets containing diverse instances of corn leaf diseases. This model is poised to transcend the limitations of human observation, offering an unprecedented level of accuracy in identifying and classifying diseases affecting corn crops. The deep learning model, a product of meticulous training, equips the application to detect and diagnose these diseases with remarkable precision. The potential impact on the agricultural sector is profound, with farmers gaining the ability to identify issues at an early stage, allowing for timely intervention and mitigation. In conclusion, the proposed deep learning model integrated into an android- based application represents a pioneering solution to a longstanding challenge in agriculture. As we navigate the complexities of modern farming, the fusion of AI and agriculture emerges as a beacon of progress, heralding a future where technology becomes an indispensable ally in the quest for global food security.