Detecting Apple Leaf Disease with EfficientNet B0 Transfer Learning Model

Apple farming is a key aspect of global agriculture, significantly contributing to both economies and nutrition on a global level. However, apple tree farming faces numerous challenges, and the major challenge is the existence of diseases that negatively impact the overall health and productivity of the trees. Leaf diseases are a serious issue since they can potentially inflict significant damage by interfering with essential physiological processes like photosynthesis and nutrient absorption. The timely and accurate detection of diseases is of critical importance to facilitate effective control and conservation of crops. While they have been found to be useful, the traditional methods of manual examination are defined by their time-consuming nature and vulnerability to mistakes. This research explores the possibility of employing EfficientNet B0, a convolutional neural network variant, in automatically detecting apple leaf diseases, by leveraging recent advances in artificial intelligence and machine learning. This research seeks to contribute to scholarship in creating an automated system capable of detecting and classifying various diseases that target apple leaves accurately. This work gives a general overview of the methodology, experiment setup, and results achieved with EfficientNet B0. The research brings to light the capacity of EfficientNet B0 in revolutionizing apple orchard disease management practices.