Automated Wheat Leaf Disease Detection Using Deep Learning with TensorFlow and Keras

Wheat leaf diseases seriously compromise world food security because of their impact on crop yields and quality. The quality and productivity of crops can be much affected by wheat leaf diseases. Good treatment of diseases depends on quick identification and accurate diagnosis. This work offers a creative approach employing TensorFlow and Keras, two powerful deep learning structures, for the evident proof of wheat leaf diseases. Convolutional neural network architecture was created to efficiently extract important features from photos of wheat leaves afflicted by different illnesses. To increase generalizability, the CNN model was taught on a collection of wheat leaf images using data augmentation methods. Images of wheat leaves stained with common illnesses including rust, blight, and mildew annotated on them train the system. The model’s efficient classification and analysis of the photos lets faster intervention and identification possible. Including transfer learning and data augmentation methods helps the model to be more generalizable. By means of automation of disease identification, this technology offers farmers and agricultural experts a scalable and efficient tool for enhancing crop health management. Thanks to its capacity to properly categorize wheat leaf diseases, as shown by the experimental findings, farmers and other agricultural experts seeking for quick diagnosis and treatment of diseases may find the suggested method to be a useful tool.