TL;DR
A new hybrid deep learning architecture is proposed by combining Convectional Neural Networks, Long Short-Term Memory (LSTM) units, and a channel-wise attention module that can address such critical challenges as inter-class visual similarity, dataset class imbalance, and environmental variability.
Cotton production is frequently affected by leaf diseases that can reduce plant productivity, deteriorate crop quality, and cause considerable financial losses for farmers. Consequently, rapid and reliable disease identification is an important requirement for precision agriculture and effective crop protection. Conven...
This study delineates five advanced convolutional neural network designs that employ deep learning for the classification of wheat weeds and determined that VGG16 is the optimal choice for precise wheat–weed categorization because to its superior generalizability and accuracy.
Corn Transfer Learning Network (CTL-Net), an end-to-end hybrid deep learning model for corn leaf disease identification, allows accurate and interpretable predictions in a computationally efficient manner, making it an excellent candidate for mobile and resource-limited applications in precision agriculture.
The research results have concluded that, in the future, lightweight models, larger-scale, more diverse data, and the integration of deep learning with IoT and autonomous systems should be the subject of research to make the processes of monitoring and controlling weeds in modern agriculture fully automated and sustain...
Accurate and timely identification of plant diseases from leaf images remains a critical challenge in precision agriculture, particularly when diseases manifest with spatially disjoint symptoms and subtle textural variations. We propose a hybrid deep learning framework that synergistically combines convolutional neural...
This study improves potato leaf disease detection using a fine-tuned InceptionV3 with data augmentation and dropout, while Grad-CAM visualizations enhance model interpretability, reliability, and practical utility for accurate agricultural disease diagnosis.
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