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- W4387489986 abstract "Mangoes have long played an important role in global import and export, but a fall in mango commerce has been seen over the past few years, preventing producers from meeting market demand. The predominant cause of this loss is the prevalence of mango tree disease, which finally transforms healthy and fruitful mango orchids into low-yield varieties. Our proposed approach offers a Mango Leaf Health Detection Dataset for mango tree health detection. The dataset has been validated and tested with YOLO v5s, YOLO v7, and YOLO v8s. The YOLO Algorithm analyzes the image of the leaf by passing it through a pipeline consisting of redefining the image in grid cells, constructing bounding boxes, and predicting the class. This not only saves time, but also delivers accurate and effective results, thereby designating the area of the orchid where pesticide must be treated. Our trained YOLO v7 detection video can be found ${color{Blue}text{here}}$, and the dataset used can be accessed ${color{Blue}text{here}}$." @default.
- W4387489986 created "2023-10-11" @default.
- W4387489986 creator A5045658663 @default.
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- W4387489986 date "2023-09-13" @default.
- W4387489986 modified "2023-10-16" @default.
- W4387489986 title "Mango Farming Optimization With AI: Boosting Cultivation Efficiency" @default.
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- W4387489986 doi "https://doi.org/10.1109/iccit58132.2023.10273915" @default.
- W4387489986 hasPublicationYear "2023" @default.
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