An intelligent computer vision-based deep learning framework for automated detection, localization, and classification of fruit diseases in precision agriculture systems
Aug 2026· Signal, Image and Video Processing· Vol 20· 0 citations· 43 references
Computer Science
TL;DR
The DL-FDD framework establishes a new performance benchmark for automated fruit disease detection, providing a scalable, interpretable, and computationally efficient solution suitable for real-world precision agriculture deployment.
Pest infestation in agriculture is a significant challenge to the world food security and production. This research uses artificial intelligence to create a deep learning pest recognition system to improve accuracy in agriculture through the implementation of pest recognition automation. They trained and tested several...
M. Rahman, Supprio Ghosh Nil, Md. Zahirul Islam et al.· International Conference Com...· 0 citations
A deep learning system of real-time detection and classification of crop leaf diseases that combines effective object detection and disease classification in a single system that allows localizing the disease and diagnosing it within a short time, which contributes to the development of sustainable and precision farmin...
S. Praveen, Karuna Arava, Tenali Nagamani et al.· International Journal of Adv...· 0 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
Artificial intelligence-based detection is positioned as a pivotal component of sustainable, high-productivity agriculture, essential for mitigating food insecurity and enhancing climate resilience through optimized resource application and ecologically conscious crop protection strategies.
Research Author· American Journal of Advanced...· 0 citations
This review synthesises AI techniques, IoT architectures, and edge-intelligence strategies within a unified framework, highlighting their combined potential and identifying future directions for robust, automated pest detection.
B. Kariyanna, Karnam Poojitha· Discover Agriculture· 0 citations
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...
F. Okoye, Njoku Camillus Ekene, E. Chidi· International journal of re...· 0 citations
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