Investigation on Object Detection Models for Plant Disease Detection Framework

Plant diseases are the most common and severe threat to precision agriculture. Therefore, identification and diagnosis of illnesses at a premature stage are vital. In addition, manual observation according to specific selection criteria is difficult and expensive. While various deep learning-based s...

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Bibliographic Details
Main Authors: R, Kavitha Lakshmi, Savarimuthu, Nickolas
Format: Conference Proceeding
Language:English
Subjects:
Biotic stress
Computational modeling
Computer Vision
Conferences
Crops
Measurement
Object detection
Pipelines
Plant disease
Plants (biology)
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Summary:Plant diseases are the most common and severe threat to precision agriculture. Therefore, identification and diagnosis of illnesses at a premature stage are vital. In addition, manual observation according to specific selection criteria is difficult and expensive. While various deep learning-based solutions have been proposed for this process, they usually suffer from lengthy training/testing times with massive datasets. In this paper, to address this problem, we explore the potential of computer vision-based object detection methods for early plant disease detection. A comparative study has been performed with three different benchmark object detection models YOLOv4, EfficientDet, Scaled-YOLOV4. The experimental results were evaluated with precision, recall, F1-score, and mean Average Precision (mAP) as performance metrics. All models are trained using the PlantVillage dataset. Empirical results show that the Scaled-YOLOv4 model is a well suitable object detection model providing a real-time solution in detecting even small infected regions of the plant leaves within less time duration. Therefore, detection and diagnosis of diseases at an early stage of infection are essential.
ISSN:2642-7354
DOI:10.1109/ICCCA52192.2021.9666441