Efficient Pest Detection Through Advanced Machine Learning Technique 

Sandhya Devi Ramiah Subburaj ^1*, Cowshik Eswaramoorthy² , Vishnu Gunasekaran Latha³  and Rakshan Kaarthi Palanisamy Chinnasamy⁴

Department of Electrical and Electronics Engineering, Kumaraguru College of Technology, Coimbatore, India

Corresponding Author E-mail:sandhyadevi.rs.eee@kct.ac.in

DOI : http://dx.doi.org/10.12944/CARJ.12.3.08

Abstract:

Crop Protection is the key element to achieve food security. Many studies have been conducted over the decades to avoid crop losses during pre- harvest and post-harvest stages. Crop losses due to pest attack and plant disease spread, reduces the agriculture production and possess a direct impact on the economy of a country.  Deployment of Artificial Intelligence (AI) based pest control strategies to detect pest species is under research. In this manuscript, deep learning based EfficientNetB7 architecture and transfer learning methodology is used to develop a real-time resource-efficient pest detection system. EfficientNetB7's innovative compound scaling technique has managed to balance efficiency in terms of computations and accuracy to effectively classify pests through images with minimal resources. The proposed system uses appropriate fine-tuning of training parameters and regularization mechanisms such as optimizers, data augmentation, to effectively develop a pest detection system. The trained model is ported on to STM32 microcontroller using CubeAI in STM32CubeIDE.The model is assessed by using a publicly available dataset, and a classification accuracy of 93.5% is achieved. This system not only automates pest detection but also provides a match percentage for the identified pests, thus supporting precision agriculture. Future work includes increasing the dataset and exploring edge AI techniques for decentralized decision-making. This manuscript showcases the transformative potential of EfficientNetB7 in precision agriculture, offering a scalable, cost-effective, and sustainable solution for the detection of pests with relevant broader impacts on agricultural automation practices.

Keywords:

Convolutional Neural Networks(CNN); deep learning; EfficientNetB7; image classification;  transfer learning

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