Neural Networks for Pest Diagnosis in Agriculture: A Global Literature Review

Agricultural pests severely reduce global crop yields. To mitigate these losses, pest identification systems based on artificial intelligence have gained importance. This review analyzes worldwide advances in the use of neural networks for agricultural pest diagnosis, covering studies from 2007 to February 2024 retrieved from the Scopus database. Data were processed in Minitab 19 and spreadsheets, and keywords were mapped with VOSviewer. Results show that India and China lead scientific output, with research focused on corn, tomato, rice, and wheat. The most common architectures are ResNet, YOLO, and VGG-16/19, achieving performance metrics of up to 99 %. The review highlights the strong relationship between economic development and the adoption of neural networks. These findings provide researchers, agricultural engineers, and policymakers with a global perspective to guide future AI-based pest management strategies and support automation, especially in developing countries.