Deep Learning-Based Identification of Strawberry Leaf Diseases with LeNet Architecture

Strawberry leaf diseases seriously affect crop output and quality; hence quick and precise identification techniques are quite important. This work presents a deep learning-based method based on LeNet convolutional neural network (CNN) architecture to detect strawberry leaf illnesses. Originally meant for handwritten digit classification, the LeNet model has been modified and refined to properly identify illnesses in strawberry leaves. High-resolution photos of strawberry leaves, categorized as healthy and diseased, make up the dataset utilized for training and validation. The LeNet model shows a remarkable accuracy of 95% on the validation set, therefore showing its potential as a dependable tool for the diagnosis of strawberry leaf disease from experimental results. Key measures including precision, recall, Fl-score, and a confusion matrix were used to assess the model's performance, thereby verifying its dependability and efficiency in separating healthy from sick leaves.