An Intelligent Approach towards Plant Leaf Disease Detection through Different Convolutional Neural Networks
Keywords:
Machine Learning (ML), Deep Learning (DL), Convolutional Neural Networks (CNN), Features extraction, Image augmentationAbstract
Identification and diagnosis of plant leaf diseases at an advanced stage and with a high degree of accuracy are essential for ensuring plant production and minimizing losses in agricultural yields, both qualitatively and quantitatively. The millions of living organisms, including plants and animals, are kept in balance with one another by many ecological processes, such as plant diseases, which are a recurrent aspect of nature. The subfield of computer vision, object recognition, has made substantial progress in recent years. Convolutional neural networks are deep learning network architecture trains using data in a hands-on manner. Our primary emphasis was making minute adjustments to the hyperparameters of well-known pre-trained models, including DenseNet-121, ResNet-50, VGG-16, and Inception V4. This study presented a convolutional neural network model for detecting and identifying plant leaf diseases based on visual data to boost accuracy, generality, and the overall efficacy of training. The outcome of the suggested model evaluates next to the results of other models. Experiments demonstrated that the proposed convolutional neural network-based model worked better than other models and achieved a classification accuracy of 99.23% higher.
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