Harvesting Insights: Transforming Wheat Crop Health Through CNNs and Transfer Learning

As global food production needs to be sustained, the agricultural sector is facing greater pressure that calls for early identification of diseases in crops. This paper intends to create an efficient and scalable deep learning model that is capable of accurate classification of the diseases infesting wheat. This study specifically targets the wheat disease symptoms, which claim that learning layers and building model architectures help in capturing more intricate patterns in the image. To tackle the problem of limited annotated data, a variety of image augmentation techniques are employed in this paper to make sure that the model generalizes well to realworld distortions. Key to our solution is a modified ResNet50 architecture, showcasing the need for domain-tailored deep learning frameworks in agricultural diagnostics. The model satisfies the accuracy necessary to determine disease patterns, which in turn provides an edge for crop monitoring and management in terms of health. Our work makes a valuable contribution to agricultural diagnostics by showcasing the utility of cutting-edge technologies like CNNs as well as transfer learning. Our study highlights the importance of technological innovations to boost global food security and calls for further progress in agricultural practices required by an accelerating world demand for food production.