Biotic Stress Management in Soil-Less Agriculture Systems: A Deep Learning Approach for Identification of Leaf Miner Pest Infestation

Abstract

Leaf miner pests pose a serious threat to the productivity, profitability, and sustainability of soil-less tomato cultivation systems. Early and accurate identification of leaf miner infestation is crucial for timely pest control measures. This study presents an efficient approach using attention-based convolutional neural networks for timely identification of this pest infestation. The proposed approach uses both spatial and channel attention modules to enhance the feature extraction capability of the convolutional neural network. The custom model developed was trained using an image dataset collected from tomatoes grown in a hydroponic environment. The different hyper parameters were tuned to get the optimal model performance. The experimental results show that the proposed attention-based CNN model achieved an overall accuracy of 97.87%, 97.10% precision, 98.53% recall, and 97.81% F1-score. Additionally, the model performance was compared with other pre-trained models viz., AlexNet, VGG16, and VGG19, and was found to outperform these state-of-the-art CNN models due to its improved feature extraction capability. The efficiency of the model underlines its potential to be deployed as part of automated pest monitoring systems in hydroponic environments. This work contributes to the development of computer vision and deep learning-based solutions for precision agriculture applications.