RIFATA: Remora improved invasive feedback artificial tree algorithm-enabled hybrid deep learning approach for root disease classification. (April 2023)

• Record Type: Journal Article
• Authors: Jackulin, C.; Murugavalli, S.; Valarmathi, K.
• Is Part Of: Biomedical signal processing and control. Volume 82(2023)
• Publication Date: 2023-04
• Subjects: Root disease -- Deep Q Network (DQN) -- Deep Residual Neural Network (DRN) -- Remora Optimization Algorithm (ROA) -- Feedback Artificial Tree Algorithm (FATA); Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2023.104578
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Plant root disease classification is very crucial for sustainable agriculture and it is very complex to monitor the diseases manually. The detection of plant diseases uses image processing since it involves a significant amount of work and takes a long time to process. The field of deep learning (DL) is exciting and has shown potential in terms of accuracy. The existing approaches failed to visualize the spot diseases. Hence, this research develops an automated system for root disease categorization utilizing a Remora Improved Feedback Artificial Tree Algorithm (RIFATA)-based hybrid deep learning model. Initially, pre-processing is executed through a Gaussian filter and root area segmentation is done under the segmentation process employing Pyramid Scene Parsing Network (PSPNet), where PSPNet is optimally tuned using RIFATA. To expand the dimensionality of data, the augmentation process is carried out using methodologies like translation, rotation, cropping, shearing, random erasing, and color space shifting. By using hybrid deep learning approaches like Deep Q Network (DQN) and Deep Residual Neural Network (DRN), which effectively train the classifiers using the same RIFATA, root disease classification is achieved. For the Alfalfa root crowns dataset, the developed RIFATA-based hybrid deep learning system achieved higher accuracy, sensitivity, and specificity of 0.941, 0.960, and 0.921. Although the generated model performs better in terms of accuracy, it was unable to categorize the different types of root diseases and would have remained a potential future approach.