Open Access

ARTICLE

YOLOCSP-PEST for Crops Pest Localization and Classification

Farooq Ali^1,*, Huma Qayyum¹, Kashif Saleem², Iftikhar Ahmad³, Muhammad Javed Iqbal⁴

1 Department of Software Engineering, University of Engineering and Technology, Taxila, 47050, Pakistan
2 Department of Computer Science & Engineering, College of Applied Studies & Community Service, King Saud University, Riyadh, 11362, Saudi Arabia
3 Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
4 Department of Computer Science, University of Engineering and Technology, Taxila, 47050, Pakistan

* Corresponding Author: Farooq Ali. Email:

Computers, Materials & Continua 2025, 82(2), 2373-2388. https://doi.org/10.32604/cmc.2025.060745

Received 08 November 2024; Accepted 16 January 2025; Issue published 17 February 2025

Abstract

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

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Keywords

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