Deep learning-based detection of co-occurring diseases in mustard (Brassica juncea) crop using yolov11

HARSH SACHAN; SUDEEP MARWAHA; MD ASHRAFUL HAQUE; SHALINI KUMARI; CHANDAN KUMAR DEB; BISHNU MAYA BASHYAL; PREETI MARWAHA

doi:10.56093/ijas.v96i5.176894

Authors

HARSH SACHAN ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India
SUDEEP MARWAHA ICAR- Central Institute of Agricultural Engineering, Bhopal, Madhya Pradesh 462 038, India
MD ASHRAFUL HAQUE ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110012
SHALINI KUMARI ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India
CHANDAN KUMAR DEB ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110012
BISHNU MAYA BASHYAL ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India
PREETI MARWAHA Acharya Narendra Dev College (University of Delhi), Govindpuri, Kalkaji, New Delhi 110 019, India

https://doi.org/10.56093/ijas.v96i5.176894

Keywords:

Co-occurring diseases, Deep learning, Disease detection, Mustard crops, YOLOv11 architecture

Abstract

Mustard (Brassica juncea (L.) Czern & Coss.) is an essential crop in agriculture, but its yield is often restricted by different kind of diseases, affecting both farmers and the edible oil industry. Recent advancements in artificial intelligence (AI) have transformed plant disease detection by enabling precise identification of various diseases. However, detection and management of diseases becomes more challenging when crops are affected by multiple co-occurring diseases. To address this challenge, deep learning algorithm is implemented for precise detection of co-occurring disease in mustard crop. In this study, a dataset consisting of 329 diseased leaf images of mustard crop was collected from the experimental fields of ICAR-Indian Agricultural Research Institute (IARI), New Delhi, during the 2023–24 growing period. The dataset includes images with co-occurring symptoms of Alternaria blight and white rust diseases. Dataset is processed through annotation and augmentation to boost detection accuracy of the network. Further, a YOLO-based detection model (YOLOv11 and it's variants) was trained and validated, and their performance was evaluated using the test dataset. Among the YOLOv11 variants, YOLOv11-x (Extra-large version) showed the highest performance by achieving mAP@50 score of 96.2% with average f1-score of 93.8% on validation data, performing 4.8% relatively better than the least complex model (YOLOv11-n), highlighting it's superior detection capability. Furthermore, YOLOv11-x also surpassed previous YOLO models (YOLOv8, YOLOv9, and YOLOv10) in detection accuracy. The experimental results demonstrate the effectiveness of YOLOv11 for co-occurring disease detection in mustard crops.

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