CERTH and University of Thessaly have proceeded to another great accomplishment within the context of PestNu project!
Their scientific article titled “AI-Driven Insect Detection, Real-Time Monitoring, and Population Forecasting in Greenhouses” was published on the “AgriEngineering” Journal by MDPI!
The authors of the article are: Dimitrios Kapetas, Panagiotis Christakakis, Sofia Faliagka, Nikolaos Katsoulas and Eleftheria Maria Pechlivaniand its respective DOI is the following: https://doi.org/10.3390/agriengineering7020029
The abstract and the keywords of the publication can be found below.
Abstract
Insecticide use in agriculture has significantly increased over the past decades, reaching 774 thousand metric tons in 2022. This widespread reliance on chemical insecticides has substantial economic, environmental, and human health consequences, highlighting the urgent need for sustainable pest management strategies. Early detection, insect monitoring, and population forecasting through Artificial Intelligence (AI)-based methods, can enable swift responsiveness, allowing for reduced but more effective insecticide use, mitigating traditional labor-intensive and error prone solutions. The main challenge is creating AI models that perform with speed and accuracy, enabling immediate farmer action. This study highlights the innovating potential of such an approach, focusing on the detection and prediction of black aphids under state-of-the-art Deep Learning (DL) models. A dataset of 220 sticky paper images was captured. The detection system employs a YOLOv10 DL model that achieved an accuracy of 89.1% (mAP50). For insect population prediction, random forests, gradient boosting, LSTM, and the ARIMA, ARIMAX, and SARIMAX models were evaluated. The ARIMAX model performed best with a Mean Square Error (MSE) of 75.61, corresponding to an average deviation of 8.61 insects per day between predicted and actual insect counts. For the visualization of the detection results, the DL model was embedded to a mobile application. This holistic approach supports early intervention strategies and sustainable pest management while offering a scalable solution for smart-agriculture environments.
Keywords: insect detection; deep learning; insect population prediction; machine learning; black aphids; mobile application; pests
You can reach the full article following this link: https://www.mdpi.com/2624-7402/7/2/29