AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
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.
Many leaf diseases have significant effects on yield and quality and wheat is an important crop contributing to food security globally. Accurate and timely diagnosis of these diseases is important for the sustainable use of agriculture. This study assesses the effectiveness of deep learning (DL) technique using ResNet...
Agriculture is a leading economical sector that contributes significantly to the livelihoods and the total Gross Domestic Product (GDP) of Pakistan. With cotton as a key crop, its production must be sustainable, and this becomes a challenge posed by environmental changes, pest infestations, and the increasing costs of...
India’s economy is heavily reliant on agriculture, with a diverse array of crops grown on vast tracts of land. Fruit cultivation, especially papaya, has become more popular in recent years because of its high nutritional and financial value. To increase yield, maximize resource use and lessen reliance on chemical pesti...
A comprehensive and systematic review of state-of-the-art methods for detecting potato leaf disease, covering convolutional neural networks, transformer-based architectures, and hybrid models, and a strategic comparative analysis is conducted.
This research proposes a hybrid model for identifying plant (groundnut) illness status using a CNN and a vision transformer (HCVT), which outperformed current cutting-edge models in identifying plant disease and demonstrated its generalization potential.
This work demonstrates ResNet50’s performance for bean leaf disease classification tasks, providing important insights for future research and practical applications in agricultural disease control.
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