Utilizing the AlexNet and Eig(Hess)-HOG Model for Identifying and Categorizing Plant Diseases
Keywords:
Eig(Hess), Machine learning , Plant diseases, HOG, AlexNet , Training precision, PCA.Abstract
Plant diseases are not only resilient but also exhibit rapid proliferation, posing a significant threat to plant health and agricultural yield. Detecting and diagnosing these diseases automatically is paramount in the field of agriculture. Many a methods have been suggested to tackle the challenge for the identification of plant diseases and diagnosis, with deep learning emerging as the favored process due to its outstanding performance. In this study, we introduce an effective methodology that leverages Machine Learning and Deep Learning a approaches. Our approach combines an AlexNet convolutional neural network (CNN) within the Hessian matrix for calculating image surface eigenvalues. Furthermore, we employ the principal component analysis (PCA) Technique for dimension reduction.
Numerous tests were conducted to assess the effectiveness of our method for classifying and detecting plant leaf diseases. We compared the production of our compare the model to other cutting-edge deep learning models, utilizing the PlantVillage dataset for model training. Our models were trained on the initial dataset and an enhanced dataset, comprising 55,448 and 61,486 images, respectively. The experimental results conclusively illustrate the excellence of our approach contrasted to current process, manifesting as improved accuracy, average precision (AP), and reduced computational complexity.
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