Automated abnormal potato plant detection system using deep learning models and portable video cameras

Applications of Computer Vision on Automatic Potato Plant Disease Detection: A Systematic Literature Review

“…It is used in this study because the review is not interested in providing rating for studies but in identifying whether an article's evaluation is positive (yes) or negative (no) for a given QA question. Te QA questions are mentioned as follows based PS4 [20] Review on potato late blight and potato tuber moth and their integrated pest management options in Ethiopia ✓ PS5 [21] Farmers' knowledge and practices of potato disease management in Ethiopia ✓ PS6 [22] Assessment of production practices of smallholder potato (Solanum tuberosum L.) farmers in Wolaita zone, Southern Ethiopia ✓ PS7 [23] Plant disease detection techniques: a review ✓ PS8 [24] Understanding digital image processing ✓ PS9 [25] Detection of plant leaf diseases using image segmentation and soft computing techniques ✓ PS10 [26] Detection and classifcation of citrus diseases in agriculture based on optimized weighted segmentation and feature selection ✓ PS11 [27] Detection of leaf diseases and classifcation using digital image processing ✓ PS12 [28] A survey on plant disease detection and classifcation using diferent machine learning algorithms ✓ PS13 [29] Plant leaf disease detection and classifcation based on CNN with the LVQ algorithm ✓ PS14 [30] Using deep learning for image-based potato tuber disease detection ✓ PS15 [31] Disease detection on the leaves of tomato plants by using deep learning ✓ PS16 [32] Tomato plant disease classifcation in digital images using classifcation tree ✓ PS17 [33] Deep learning for tomato diseases-classifcation and symptoms visualization ✓ PS18 [34] Automatic detection and classifcation of leaf spot disease in sugar beet using deep learning algorithms ✓ PS19 [35] Identifcation of rice diseases using deep convolutional neural networks ✓ PS20 [36] Automated abnormal potato plant detection system using deep learning models and portable video cameras ✓ PS21 [37] Blackleg detection in potato plants using convolutional neural networks ✓ PS22 [38] Recognition of early blight and late blight diseases on potato leaves based on graph cut segmentation ✓ PS23 [39] Potato leaf diseases detection using deep learning ✓ PS24 [40] Plant identifcation using deep neural networks via optimization of transfer learning parameters ✓ PS25 [41] Automatic late blight lesion recognition and severity quantifcation based on feld imagery of diverse potato genotypes by deep learning ✓ PS26 [42] Plant leaf detection and disea...…”

“…Generally, the learning process of deep learning can be unsupervised, supervised, or semisupervised based on the nature of the problem at hand. Potato tuber [36] Not mentioned Leaf [37] Blackleg Leaf [38] GD and SD early blight, late blight Leaf [39] Early blight and late blight Leaf [41] Late blight Leaf Note: BS � black scurf, SS � silver scurf, SD � serious degree, and GD � general degree.…”

“…Since there are many types of AFs, selecting the right one deserves a critical decision as this will afect the performance of the neural network [73]. Sardogan et al [29], Reddy et al [69], Yadhav et al [76], and Afework and Debelee [77] used diferent AFs such as CNN Custom prepared Accuracy (96%) [36] Fast R-CNN and YOLO v3…”

Detection of the viral disease on the potato foliar and tubers using a machine learning approach

“…The potato yield will be good only in the regions where temperatures are less. In India, "The viruses like Potato virus Y (PVY), Potato leaf roll virus (PLRV), Tomato leaf curl New Delhi virus (ToLCNDV) and Groundnut bud necrosis virus (GBNV) is a major threat to the production of potatoes" [22].…”

Postharvest Authentication of Potato Cultivars Using Machine Learning to Provide High-Quality Products

“…Machine vision is used to recognize vegetables effectively [7]. However, in some cases, the application of deep learning can improve the accuracy of image recognition [8]. Based on the available literature, machine learning algorithms were used, e.g., for the cultivar identification of whole potato tubers [9,10], as well as the cultivar discrimination of raw and processed flesh (slices) of potato [11].…”