The proposed CropNet++ViT model is evaluated on three benchmark datasets, achieving classification accuracies and uncertainty analysis confirms a clear separation between correct and incorrect predictions, establishing the proposed CropNet++ViT model as a scalable, interpretable, and uncertainty-aware solution for automated fruit grading in smart agriculture.
This study proposes a hybrid technique that integrates attention-weighted exponential pooling (AWEP) with CNN and Vision Transformer (ViT) to enhance feature representation and significantly improve classification performance and highlights that ViT improves embedding separability through t‑distributed stochastic neigh...
Maddassar Jalal, Amandeep Kaur· Applied Fruit Science· 0 citations
A novel hybrid deep learning framework integrating Convolutional Neural Networks, Transformer-based attention mechanisms, and Long Short-Term Memory networks for spatio-temporal cotton leaf disease detection and classification is proposed, suitable for intelligent precision agriculture systems and real-time disease mon...
Prajakta Sunil Gupta, A. V. Zade· International journal of com...· 0 citations
The proposed AgriX-SENet framework effectively combines high classification performance with model interpretability, addressing a key limitation of existing CNN-based plant disease detection systems and making it a promising solution for scalable agricultural diagnostics.
S. Raj, Prashant Johri, Vishwadeepak Singh Baghela et al.· Frontiers in Plant Science· 2 citations
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.
K. P. Praveen Kumar, Y. Kuma· Engineering, Technology &...· 0 citations
Accurate identification of millet varieties was essential for improving food quality assessment, agricultural productivity, post-harvest management, and intelligent grain processing systems. Conventional manual classification methods are often time-consuming, dependent on expert knowledge, and affected by visual simila...
Rajasekaran Saminatha, Rajat Verma, Ashwini Lokhande et al.· International journal of com...· 0 citations
Early detection of plant leaf diseases is critical for minimizing crop losses and supporting precision agriculture. While Convolutional Neural Networks (CNNs) have demonstrated high accuracy in image-based diagnosis, conventional architectures may not optimally balance spatial localization and channel-wise feature refi...
A. Anton, S. Rustad, G. F. Shidik et al.· International Journal of Adv...· 0 citations
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