Intelligent agrobots for crop yield estimation using computer vision

The machine vision-based autonomous intelligent robots perform precise farm tasks suchas robot harvesting, weeding, pest or fertilizer spraying, monitoring, and pruning. Estimating crop yield is an essential assignment on a regional or federal scale. For a long timethe estimation measures were based on the statistics from manual counting of plants ina specific zone. The computer vision algorithms have addressed the technical drawbacksof the conventional image processing techniques and established an autonomous disciplineand yielded new approaches to crop planning. A method for quantitative assessment ofa tomato crop has been developed in this research using color thresholding in MATLAB using the RGB color model. Converting an RGB image to a grayscale image is one of thesteps involved in detecting red color in a taken image. After subtracting the two images,a median filter is employed to filter the noisy pixels to produce a two-dimensional blackand white image. The bounding boxes are used to label the binary digital images to detectrelated components, and the parameters of the labeled regions are computed to measurethe number of tomatoes in a crop. The obtained R2 correlation coefficient between thetomato berry counting algorithm and human counting was 0.98. Furthermore, the color ofeach pixel in the acquired image is evaluated by examining RGB values for pixel intensitiesin the obtained image. The performance of the berry counting algorithm was evaluated,and the technique was determined to have a high precision and recognition ratio of 96%.The research indicates that this technique may be used to estimate the crop yield, whichis helpful information for forecasting yields, planning harvest plans, and generating prescription maps for field-specific management strategies. The proposed model performedexceptionally well in estimating yield with each tomato (Solanum lycopersicum) crop.

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Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).