Small Pests Detection in Field Crops Using Deep Learning Object Detection

Saim Khalid, Hadi Mohsen Oqaibi, Muhammad Aqib () and Yaser Hafeez
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Saim Khalid: University Institute of Information Technology, PMAS-Arid Agriculture University Rawalpindi, Rawalpindi 46300, Pakistan
Hadi Mohsen Oqaibi: IS Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Muhammad Aqib: University Institute of Information Technology, PMAS-Arid Agriculture University Rawalpindi, Rawalpindi 46300, Pakistan
Yaser Hafeez: University Institute of Information Technology, PMAS-Arid Agriculture University Rawalpindi, Rawalpindi 46300, Pakistan

Sustainability, 2023, vol. 15, issue 8, 1-19

Abstract: Deep learning algorithms, such as convolutional neural networks (CNNs), have been widely studied and applied in various fields including agriculture. Agriculture is the most important source of food and income in human life. In most countries, the backbone of the economy is based on agriculture. Pests are one of the major challenges in crop production worldwide. To reduce the overall production and economic loss from pests, advancement in computer vision and artificial intelligence may lead to early and small pest detection with greater accuracy and speed. In this paper, an approach for early pest detection using deep learning and convolutional neural networks has been presented. Object detection is applied on a dataset with images of thistle caterpillars, red beetles, and citrus psylla. The input dataset contains 9875 images of all the pests under different illumination conditions. State-of-the-art Yolo v3, Yolov3-Tiny, Yolov4, Yolov4-Tiny, Yolov6, and Yolov8 have been adopted in this study for detection. All of these models were selected based on their performance in object detection. The images were annotated in the Yolo format. Yolov8 achieved the highest mAP of 84.7% with an average loss of 0.7939, which is better than the results reported in other works when compared to small pest detection. The Yolov8 model was further integrated in an Android application for real time pest detection. This paper contributes the implementation of novel deep learning models, analytical methodology, and a workflow to detect pests in crops for effective pest management.

Keywords: deep learning; convolution neural networks; object detection; image classification; pest detection; pest management (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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