Abstract
In precision agriculture (PA), the usage of image processing, artificial intelligence, data analysis, and internet of things provides an increase in efficiency, energy, and time saving. In image processing–based applications, vegetation detection, in other words, segmentation that allows monitoring of plant growth and health as well as identification of weeds has a great importance. Vegetation indices (VIs) are widely used algorithms for segmentation. Their advantages include low computational cost and easy implementation and handling compared to the other algorithms. Nevertheless, they require a manual threshold detection that customizes the process and prevents generalization. In this study, a novel automatic segmentation method, which does not require a manual threshold detection by combining VIs with a classification algorithm, is proposed. It deals with the segmentation process as a two class classification problem (vegetation and background). As the classification algorithm, Discriminative Common Vector Approach (DCVA) that has a high discrimination power is used. Each image pixel is represented with a 3 × 1 dimensional vector whose elements correspond to Excess Green (ExG), Green minus Blue (GB), and Color Index of Vegetation (CIVE); VI values are obtained. Then, on the sample space accepting this pixel vector as a sample, DCVA is applied and a discriminative common vector for each class which is unique and describes that class in the best way possible is obtained and it is used for classification. Proposed segmentation method’s performance is compared with Convolutional Neural Networks (CNN) and Random Forest (RF) algorithm. The proposed segmentation algorithm outperformed both CNN’s and RF’s performance.
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Data availability
The datasets analyzed during the current study are available in the [CWFID—Crop Weed Field Image Dataset] repository, [https://github.com/cwfid], and in the [lameski/rgbweeddetection] [https://github.com/lameski/rgbweeddetection].
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Turhal, U.C. Vegetation detection using vegetation indices algorithm supported by statistical machine learning. Environ Monit Assess 194, 826 (2022). https://doi.org/10.1007/s10661-022-10425-w
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DOI: https://doi.org/10.1007/s10661-022-10425-w