An Attention based Vision Transformer for the Detection of Insect Pests in Castor Crop

Nitin (Indira Gandhi University, Meerpur, Rewari, India)
Satinder Bal Gupta (Professor, Indira Gandhi University, Rewari, India)
Pankaj Kumar Tyagi (Professor, Department of Biotechnology, N.I.E.T., Greater Noida, India)
Ravi Yadav (Research Scholar, Department of Computer Science & Engineering, Indira Gandhi University, Rewari, Haryana, India)
Amit Kumar Singh (Department of Computer Science & Application, Maharshi Dayanand University, Rohtak, Haryana, India)
Ajay Kumar (S.O.E.T., Raffles University, Neemrana, India)
Shiv Kant (Greater Noida Institute of Technology (GNIOT), Greater Noida)

EMITTER International Journal of Engineering Technology
Vol 14 No 1 (2026)

Castor (Ricinus communis L.) is a significant crop valued for its non-edible oil, yet its economic importance is compromised by insect pests causing substantial yield losses of 35-40%. This paper explores the efficiency of utilization of vision transformers for efficient pest classification. We propose CASTIPestViT, a Vision Transformer-based model specifically designed for insect pest detection in castor crops. The model integrates transfer learning and fine-tuning mechanisms, leveraging a pre-trained Vision Transformer (ViT) initially trained on ImageNet1k, and is fine-tuned on a custom dataset of castor insect pests. CASTIPestViT uses the self-attention mechanism of ViTs to capture global and local features of insect pests. The performance of CASTIPestViT is compared with six different pre-trained CNN models. The results obtained by the proposed model achieve a validation accuracy of 97.60% in insect pest detection and outperforming other state-of-the-art models in terms of precision, accuracy, and f1-score. The model offers a robust solution in early-stage insect pest detection to reduce yield losses. The efficiency and accuracy of the model make it suitable in sustainable crop management and smart agriculture systems for yield optimization.