Abstract
Weed is the main field element in agriculture that has an impact on crop quality and productivity. So, it is crucial to find and categorize weeds in the field when they are still in the early stages of development. Farmers often use cultural, biological and mechanical approaches to prevent weed development in their fields. Later, as technology developed, farmers started use chemical substances like herbicides and insecticides to control pests and weeds in their fields. Farmers also sprinkle herbicides on the crops after evenly spraying them throughout a field. Crop growth, crop quality and crop output are all impacted by the herbicides’ chemical composition. Therefore, it is crucial to find weed in the field when it is still in the early stages of development. Herbicides must be sprayed selectively on weeds in order to prevent damage to crops from the herbicides’ chemical components. This allows for site-specific weed control. We are proposing YOLOv5 model to detect crop and weed from the images. In this paper, we compared the performance of versions with the various existing deep learning-based object detection methods like YOLOv3, YOLOv3-tiny YOLOv3-spp with three different parameters named map 0.5, map 0.5:0.95 and dataset used. This information will be helpful for practitioners to select the best technique for the crop and weed dataset.
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Koushik, K., Venkata Suryanarayana, S. (2023). Crop and Weed Detection From Images Using YOLOv5 Family. In: Sharma, D.K., Peng, SL., Sharma, R., Jeon, G. (eds) Micro-Electronics and Telecommunication Engineering . Lecture Notes in Networks and Systems, vol 617. Springer, Singapore. https://doi.org/10.1007/978-981-19-9512-5_50
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DOI: https://doi.org/10.1007/978-981-19-9512-5_50
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