A Patch-Image Based Classification Approach for Detection of Weeds in Sugar Beet Crop
نویسندگان
چکیده
Weeds affects crops health as it shares water and nutrients from the soil, a result decreases crop yield. Manual weedicide spray through bag-pack is hazardous to human health. Localized autonomous aerial spraying units can help save water, chemical effect less on Such systems require multi-spectral cues classify crop, weed, soil surface. Our focus in this paper detection of weeds sugar beet using air-borne multispectral camera sensors, which considered an alternative sugarcane obtain Pakistan. We developed new framework for weed identification; patch-based classification approach appose semantic segmentation that more realistic real-time intelligent systems. converts 3-class pixel problem into 2-class crop-weed patch turns improves accuracy. For classification, we VGG-Beet convolutional neural network (CNN), based generic CNN (VGG16) model with 11 layers. experiments, captured dataset 3-channel sensor ground sampling distance (GSD) 0.2 cm/pixel height 4 meters. better comparison, used two publicly available imagery datasets, 5-channel 4-Channel 1cm 10 observed method robust different lighting conditions. To produce low cost system usage Agrocam recommended, higher accuracy Red Edge Sequoia sensors channels should be deployed. lower testing time our compared state-of-the-art UNet Deeplab networks.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2021
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2021.3109015