Automated Detection of Kashmiri Apple Leaf Diseases Using EfficientNetB3 Deep Learning Model
Atul Jindal, Amandeep Singh, Ankit Bansal, Monica Gupta, Nitika Verma, Pratham Kaushik
Abstract
This research aims to develop a model to detect diseases in the leaves of apples in the Kashmir region with the help of the EfficientNetB3 model. The dataset of 419 images of leaf diseases of apples caused by four diseases-Apple Rot Leaves, Healthy Leaves, Leaf Blotch, and Scab Leaves-was used for model evaluation and training. The dataset was preprocessed by the application of resizing, normalizing, and enhancing images to increase the performance of the model. The EfficientNetB3 model was trained to 87% accuracy in 25 epochs with high precision and recall of detecting Apple Rot Leaves and Healthy Leaves. However, difficulty was recorded in the detection of Leaf Blotch and Scab Leaves, with some confusion between the two. The performance of the model was validated using the help of several parameters, like precision, recall, F1score, and a confusion matrix that showed areas where it needed to be improved. This research shows how effective deep learning can be for detecting diseases in agriculture, and therefore, it could play an important role in diagnosing diseases earlier and managing crops.