Using under-canopy cotton imagery for cotton variety classification

Hussein Gharakhani; J. Alex Thomasson; Peyman Nematzadeh; Pappu Kumar Yadav; Steve Hague

doi:10.1117/12.2623034

3 June 2022 Using under-canopy cotton imagery for cotton variety classification

Hussein Gharakhani, J. Alex Thomasson, Peyman Nematzadeh, Pappu Kumar Yadav, Steve Hague

Proceedings Volume 12114, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VII; 121140B (2022) https://doi.org/10.1117/12.2623034
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Hussein Gharakhani, J. Alex Thomasson, Peyman Nematzadeh, Pappu Kumar Yadav, and Steve Hague "Using under-canopy cotton imagery for cotton variety classification", Proc. SPIE 12114, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VII, 121140B (3 June 2022); https://doi.org/10.1117/12.2623034

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