An approach for characterization of infected area in tomato leaf disease based on deep learning and object detection technique. (October 2022)

• Record Type: Journal Article
• Authors: Kaur, Prabhjot; Harnal, Shilpi; Gautam, Vinay; Singh, Mukund Pratap; Singh, Santar Pal
• Is Part Of: Engineering applications of artificial intelligence. Volume 115(2022)
• Publication Date: 2022-10
• Subjects: Ensemble deep learning -- Feature network -- Mask R-CNN -- Object detection -- Image processing; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105210
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Tomato leaf infections are a common threat to long-term tomato production that affects many farmers worldwide. Early detection, treatment, and solution of tomato leaf specificity are critical for promoting healthy tomato plant growth and ensuring ample supply and health security for the world's geometric growth (population). The detection of plant leaf disease using computer-assisted technologies is prevalent these days. In this work, use the 1610 tomato leaf images of different classes from PlantVillage standard repository for the localization of objects. An effective Deep Learning (DL) modified Mask Region Convolutional Neural Network (Mask R-CNN) is proposed for the autonomous segmentation and detection of tomato plant leaf disease in this research. Intending to conserve memory space and computational expense, the suggested model adds a light head "Region Convolutional Neural Network (R-CNN)". By varying the proportions of anchor in the RPN network and also changing the feature extraction topology, which improves the detection accuracy and computing the metric performance. The proposed technique is compared to existing state-of-the-art models to check if it is viable and robust. The outcomes of the suggested model achieved the results in terms of Mean Average Precision (mAP), F1-score, and accuracy of 0.88, 0.912, and 0.98, respectively. Furthermore, as the model's ability increases with some parameters, the detection time for lesion detection is reduced by two times than the existing models.