Cotton Boll Segmentation, Detection and Counting Using a Thresholding - HSV Algorithm
Keywords:
Saturation, HSV algorithm, cotton boll detection, cotton boll counting, histogram S channel.Abstract
In contemporary cotton farming and agriculture, where accurate yield estimation is essential for maximizing resource allocation and crop management, comprehensive cotton boll recognition and counting technology has significant applications. Enhancing agricultural production, cutting waste, and addressing labor shortages are critical in order to support cotton farming operations' sustainability and profitability. Accurate cotton boll yield estimation is highly valued in agriculture since it influences decisions about crop management and resource allocation. The S channel of the HSV (Hue, Saturation, Value) color scheme has been used for effective boll recognition. It employs clever thresholding and contour detection technology for extremely precise boll counting. To begin the inquiry, high-resolution images of cotton fields must be obtained. Then, through a laborious thresholding process, the cotton bolls are successfully separated from their background to create binary images. This binary encoding holds the key to accurate boll detection. The maximum level of accuracy in boll counting is achieved using OpenCV's find contours These images undergo laborious pre-processing to extract the S channel, which preserves the essential color and texture details unique to cotton bolls. A powerful contour detection technique is then applied to identify individual bolls within the binary images. The architecture of the algorithm accounts for variations in boll size, shape, and orientation, ensuring the robustness of the solution across a wide range of cotton field conditions. The model, with an impressive 95% accuracy rate, highlights the efficacy and relevance of the proposed methodology. This advancement holds the potential to revolutionize the calculation of cotton output in agriculture.
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