Constitutive Artificial Neural Network espoused Plant Leaf Disease Detection

Abstract

The emergence of new diseases on plant leaves poses a substantial threat to global food safety and agricultural productivity. To mitigate this risk, accurate and swift detection of plant illnesses is crucial, reducing unnecessary expenses and minimizing financial losses and environmental damage. This study proposes a method called Plant Leaf Disease Detection with a Constitutive Artificial Neural Network (PLDD-CANN) to provide advancements in deep learning. The approach begins by gathering data from the Plant Village dataset and subjecting it to pre-processing techniques. This includes noise removal and image enhancement using a Variational Marginalized Particle Filter (AVMPF). Next, an Adaptive Convex Clustering (ACC) method is employed for image segmentation, followed by feature extraction using Fast Fourier and Continuous Wavelet (FFCWT) transforms. Finally, a Constitutive Artificial Neural Network (CANN) is utilized to categorize the input image to one of several categories, including healthy and various disease types like Yellow Leaf Curl Virus, Septoria Leaf Spot, Two-Spotted Spider Mite, Bacterial Spot, Target Spot, Leaf Mold, Mosaic Virus, Early Blight, and Late Blight. Then, the proposed technique is simulated using Python under several performance metrics including precision, f1-score, error rate accuracy, sensitivity, specificity and ROC. The proposed PLDD-CANN method provides 26.75%, 25.83% and 27.46% higher accuracy comparing with existing methods an enhanced CNN technique for plant leaves disease diagnosis in tomato (CNN-PLDD), A Novel Approach for Plant Leaf Disease Predictions with Recurrent Neural Network RNN Classification Method (RNN-PLDD), Detection of tomato leaf diseases for agro-based industries (FRCNN-PLDD) respectively.