Year: 2025
Type: article
Abstract: Date palm trees, a crop of immense cultural and economic significance in the Middle East, are seriously threatened by insect pests. These pests inflict substantial damage on tree health and fruit yields, with infestations often progressing undetected until irreversible harm has occurred. The urgency of this issue is magnified by FAO estimates attributing 20-40% of global food loss to plant pests and diseases, highlighting an acute need for advanced detection methodologies. This review paper combines current research on the use of deep learning-based convolutional neural networks (CNNs) to automatically identify and classify palm tree pests using image analysis. The paper examines how a model of CNN models, trained on a variety of datasets of leaf imagery, demonstrates exceptional effectiveness in monitoring early and precise indicators of infection, such as leaf wilting, discoloration, and deformation. By combining these systems, farmers can manage pests in a revolutionary way and acquire the skills to respond accurately and in real time. With increasing demand for food and environmental concerns, this technological revolution is essential to improve crop resilience, ensure high productivity, and promote sustainable farming practices.
Authors: Maryam A. Muter, Abbas H. H. Al-Asadi
Institution: University of Basrah
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $150
OpenAlex ID: W7118003390
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