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- W4361004026 abstract "Machine learning technology nowadays is used in agriculture to detect weeds. The weed detection system can depend upon the picture patches on crops, soils, and weeds. The algorithm figures out the weeds and crops on a predefined plant basis in every image, and it also detects where weeds are placed on each row and the distance between them. It is one of the most challenging tasks in the agriculture sector. The input is based on the 4-channel NIR + RGB or regular RGB images, and it also depends on which type of sensor we are placing on the robots. We take the input and find the weeds using machine learning techniques. In this process, we train a model with extensive data set of weeds and crops of the cotton crop(which can be later applied to many different crops) and try to increase the accuracy of detecting a weed perfectly and precisely. The SSD Mobilenet model we used here was a machine learning algorithm with an accuracy of up to 90% to 95%." @default.
- W4361004026 created "2023-03-30" @default.
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- W4361004026 date "2023-03-01" @default.
- W4361004026 modified "2023-10-12" @default.
- W4361004026 title "Weed detection in agriculture crop using unmanned aerial vehicle and machine learning" @default.
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- W4361004026 doi "https://doi.org/10.1016/j.matpr.2023.03.350" @default.
- W4361004026 hasPublicationYear "2023" @default.
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