A novel hybrid segmentation technique for identification of wheat rust diseases

• Published in: Multimedia tools and applications 2024-02, Vol.83 (29), p.72221-72251
• Main Authors: Kumar, Deepak, Kukreja, Vinay, Singh, Amitoj
• Format: Article
• Language: English
• Subjects: Computer Communication Networks; Computer Science; Crop diseases; Data Structures and Information Theory; Image segmentation; Multimedia Information Systems; Plant diseases; Real time; Special Purpose and Application-Based Systems; Weather; Wheat
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-024-18463-x

Wheat is one of the most common staple crops and every year, a million tons have been exported from India worldwide. Among all states of India, more than 50% of wheat grew in different regions of Punjab. According to the Indian council of agricultural research (“Dare wheat annual report,” n.d.), every year more than 10% growth rate of the wheat crop has decreased due to wheat rust diseases (WRD). Thus, the monitoring of the wheat crop diseases is important. This paper presents a panoptic segmentation-based hybrid approach namely as FERSPNET-50 approach for WRD detection in real-time images and a dataset was collected with different weather conditions in various regions of Punjab. With the help of the GNet model, the collected images have been classified into three classes of weather conditions and then this classified weather image has been fed as input to a faster region-based convolutional model (FRCNN). FRCNN model detects the wheat leaves and stems patches. As a part of the FRCNN training process, a semi-automatic annotation method is designed for generating ground truth rust lines. To further improve the accuracy of rust detections over the whole image, a pyramid scene parsing network has been employed to predict the rust diseases at the local level. Additionally, a deep CNN pretrained model has been utilized to determine the orientation of rust segments within each detected patch, and patches that don't support local level patches are viewed as false positives. After the classification of each patch, the amount of rust severity in wheat has been calculated. The experimental results show that the proposed approach has a high precision (0.97) as compared to state of art based YOLOV4(0.88) and RetinaNet (0.82) models for WRD detection.