AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Various pre-trained deep learning models, including DenseNet121, DenseNet201, ResNet50, VGG16, VGG19, and InceptionResNetV2 are explored to identify rice plant diseases and nutrient deficiencies, with Notably, a fuzzy distance-based ensemble of DenseNet121, DenseNet201, and VGG16 achieved the highest testing accuracy.
This work proposes an ensemble transfer learning approach that combines the advances of Inception V3 and shallow convolutional neural network models that outperforms the state-of-the-art models, offering significant value in addressing agricultural challenges.
Various convolutional neural network architectures, including AlexNet, VGGNet, ResNet, DenseNet, EfficientNet, MobileNet, Inception, and Xception, are critically reviewed along with modern transformer-based models such as Vision Transformer (ViT), Swin Transformer, and hybrid CNN–Transformer frameworks.
The Plant Disease Detecting System leverages advances in artificial intelligence and deep learning to provide an automated, efficient, and reliable solution for identifying plant diseases at an early stage and contributes to increased crop productivity, reduced chemical usage, and sustainable farming practices.
It is suggested that in order to be implementable in the field, future intelligent agricultural diagnosis systems must be able to balance predictive accuracy, explainability, computational efficiency and field adaptability.
The proposed framework involves image preprocessing, feature extraction, classification and severity estimation, which would enable the accurate identification of various types of disease and estimation of their severity levels while detecting nutrient deficiencies while detecting nutrient deficiencies.
Two deep learning models, a custom CNN model and a transfer learning model based on the DenseNet-121 architecture are presented, showing that the developed transfer learning model based on DenseNet-121 had superior performance over the CNN model.
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