2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 20 , Issue 2 , PP: 38-52, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmed A. F. Osman 1 * , Rajit Nair 2 , Mosleh Hmoud Al-Adhaileh 3 , Theyazn H.H Aldhyani 4 , Saad M. AbdelRahman 5 , Sami A. Morsi 6
Doi: https://doi.org/10.54216/FPA.200204
This research introduces a cutting-edge deep learning-based agricultural engineering illness diagnosis approach. Convolutional neural networks (CNNs) and improved methods improve accuracy and efficiency. The recommended solution includes network settings, convolution processes, and sharing strategies to reduce dimensions. These methods reduce the network's processing power so it can concentrate on disease characteristics. The model employs dropout regularization, attention processes, and multi-scale feature extraction to enhance sickness prediction. The technology also utilizes photographs and sensor data to adapt to agricultural circumstances. The performance test shows that the suggested technique outperforms traditional machine learning and mixed models in F1 score (95%), accuracy (95%), precision (94%), memory (96%), and correctness (94%). It has high discriminative power with an AUC-ROC score of 0.98. The model uses computers well: two hours to train, two seconds to derive conclusions, and 65% of the CPU at all times. Real-time farming could benefit from its use. The suggested technique can properly and reliably diagnose illnesses due to its low overfitting rate and excellent generalization potential. The precision agriculture technique will enhance crop health management and productivity.
Agricultural biotechnology , Convolutional neural networks , Disease detection , Dropout regularization , Feature extraction , Image processing , Multi-modal data , Precision agriculture , Resource utilization , Real-time deployment
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