Deep Learning Approach for Detection and Prediction of Pest Infections on Plants in Greenhouses

Abstract

Pest infestations remain problematic in greenhouse agriculture, lowering yields and increasing costs. Manual pest monitoring is laborious, slow, and error-prone, resulting in delayed interventions and excessive pesticide applications. The main objective of this research was to develop an AI-driven hybrid deep learning model for automated pest detection and outbreak prediction, integrating Convolutional Neural Networks (CNNs) for image-based classification and Long Short-Term Memory (LSTM) networks for forecasting to improve response efficiency. This study relied on secondary datasets, such as PlantVillage and IP02, owing to constraints in obtaining primary datasets. These datasets provided pre-annotated pest images and historical climate trends, guaranteeing model robustness. Although the model was trained using secondary data, the study was contextualized by greenhouses in Limuru, Naivasha, and Thika; areas where pest control is an ongoing challenge. To enhance the model's ability to generalize and perform well in an array of agricultural environments, a stratified sampling method which considered farm size and agroclimatic differences was used. Techniques such as image augmentation, noise reduction, and normalization of features were utilized to further improve the quality of the data before the model was trained. Model training and optimization were performed in a GPU-enabled Google Colab environment, which supported batch processing, early stopping, and fine-tuning of hyperparameters. The hybrid model achieved 94.7% accuracy, 93.6% precision, 92.8% recall, and a 93.2% F1-score. With a Mean Absolute Error (MAE) of 0.14 and an R² score of 0.89, the LSTM forecasting module demonstrated its efficiency. This hybrid approach enables early pest identification, preventative actions, and reduced pesticide use.