Unlike above-canopy imagery, under-canopy imagery is rarely used in agricultural research, but it can provide specific information about plants that above-canopy images may not carry, such as fruiting behavior as well as early nutrient deficiencies or diseases. In this research, under-canopy images of seven cotton varieties were collected with the goal of classification according to variety. An RGB FLIR camera was installed on a remotely controlled ground robot such that the camera was close to the ground looking upwards. A deep learning-based VGG16 network was used to extract features, and a softmax classifier in the network was trained to classify the images. The VGG16 network was able to classify the seven cotton varieties with 86% accuracy. The classification accuracy based on the area under the curve (AUC) of receiver operating characteristic (ROC) showed that the images belonging to the varieties CVTI-108, 110, 114, and 120 were the most accurately (AUC = 1.0) classified, while the images of variety CVT-115 were the least accurately (AUC = 0.93) classified.
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