This paper presents the use of machine learning techniques for the development of a methodology for the analysis of digital images of citrus plants collected from unmanned aerial vehicles UAVs Proven ground based sensors including a chlorophyll meter water potential meter and spectroradiometer are used to evaluate the condition of the plants thus providing the ground truth The collected
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Get PriceThe Plant Phenotyping Datasets are intended for the development and evaluation of computer vision and machine learning algorithms such as in parenthesis we point to general category of computer vision problems that these datasets can also be used for plant detection and localization multi instance detection/localization plant segmentation foreground to background segmentation leaf
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Get PriceTheir top view images were acquired using a camera at six growth stages The corresponding plant N concentration was determined destructively The quantitative relationships between the nitrogen nutrition index NNI and the image based phenotyping features were established using the following algorithms random forest RF support vector regression SVR and neural network NN The results
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