Early detection of citrus leafminer (Phyllocnistis citrella) in lemon trees (Citrus aurantifolia) using artificial intelligence

Sánchez-Chero M.; Gutiérrez-Segura F.; Granda-Wong C.; Aguilar-Ibañez L. · Ciencia e Agrotecnologia (2026) · DOI: 10.1590/1413-7054202650000726

Resumen

The lemon tree (Citrus aurantifolia) is a highly commercial crop in northern Peru and is severely affected by the citrus leafminer pest (Phyllocnistis citrella). Early detection is vital to reduce yield losses caused by this pest. This research evaluated Machine Learning (SVM, KNN, DT, RF) and Deep Learning (CNN) algorithms to identify the presence of the leafminer across three stages of damage severity (Stage 1, Stage 2, Stage 3) and healthy leaves (No leafminer). A dataset of 1,134 field-acquired images was used. The base CNN achieved 85.71% accuracy. Through architectural optimization (adding three convolutional layers with 64, 128, and 256 filters and increasing the dense layer to 256 neurons), the optimized CNN reached 90.95% accuracy after 50 training epochs, outperforming traditional ML algorithms (79.05–84.29%). A benchmark using Google’s Teachable Machine (AutoML) platform achieved 96.86% overall accuracy, with class-specific accuracies of 94% (Stage 1), 96% (Stage 2), 97% (Stage 3), and 100% (No leafminer). Functional web and Android mobile prototypes integrating the optimized CNN model were developed for real-time field detection. These results demonstrate that the proposed architectural modifications yield superior pest detection performance, and that AutoML platforms can provide even higher accuracy, reducing technical barriers for farmers and researchers.

Instituciones: Universidad Nacional de Piura; Universidad Nacional de Frontera