Automated detection of tuta absoluta (Meyrik) lesions on tomato plants using artificial intelligence

The invasive tomato pest Tuta absoluta (Meyrik) poses a significant threat to global agriculture, often resulting in severe yield losses if not detected and managed early. This study investigates the application of artificial intelligence (AI) to develop an automated system for detecting T. absoluta (Meyrik) lesions on tomato plants. Leveraging open-source computational tools such as Google Colab, the research aims to provide an accessible and efficient solution through computational experiments, without requiring field trials.

A curated dataset of tomato plant images is prepared for training and evaluation. The YOLO (You Only Look Once) model is utilized for its proven effectiveness in small-object detection tasks, making it an ideal choice for identifying pest lesions. Model performance is assessed using metrics such as mean Average Precision (mAP), precision, recall, and F1-score, ensuring robust and reliable results across varying conditions. Prior research has highlighted the success of similar AI-based approaches in agricultural pest detection, achieving high accuracy while supporting sustainable farming practices  

This work emphasises leveraging multi-source data and advanced modelling approaches to enhance agricultural sustainability. By integrating sensing data and AI techniques, the study supports improved Integrated Pest Management (IPM) strategies, offering a scalable and environmentally friendly solution for pest monitoring in tomato production. Furthermore, the approach demonstrates how AI-driven insights from remote sensing can contribute to the broader goals of ecosystem productivity and nature-based solutions for climate change mitigation.

Acknowledgements: The authors acknowledge the support of the Project “LIFE23-CCA-ES-LIFE ACCLIMATE: Cultivating Resilience: Climate Change Adaptation Strategies for Greenhouses to Enhance Yield and Resource Efficiency from the Programme for the Environment and Climate Action (LIFE-EU) (project number: 101157315).