基于数字图像处理的蘑菇生长速度计算人工智能算法的发展

Chuan-Pin Lu, Zheng-Yang Wu
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引用次数: 0

摘要

蘑菇的生长取决于温室的小气候。温室的环境控制系统无法监控蘑菇的生长。因此,小气候的控制不是为了蘑菇的生长,而是为了农民的感受或体验。为了开发蘑菇生长的智能监测系统,本研究提出了一种基于数字图像处理的人工智能算法,实现蘑菇的自动定位和菌毛圈的自动计算。与文献中的方法相比,该方法采用了低成本的图像分析算法来计算比例圆。该方法的优点是使用低成本的计算机或嵌入式系统,大大降低了智能图像系统的部署成本和利用率。该方法利用贝叶斯分类器将目标与背景分离,提高了蘑菇定位的精度。然后,利用图像预处理、霍夫圆变换和自主开发的基于圆的区域匹配算法对菌菇进行定位,并根据找到的菌菇圆确定菌菇大小。为了验证所提方法在菌毛圆定位精度方面的有效性,所提方法的平均精度为87.0%,比传统的圆霍夫变换方法提高了60.7%。其定位稳定性优于圆形霍夫变换,单幅图像的平均运行时间为2.3 s。结果表明,该方法的有效性满足了蘑菇栽培的实际要求。
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Development of Artificial Intelligence Algorithm based on Digital Image Processing for Calculating Growth Rate of Mushrooms
Mushroom growth depends on the microclimate in greenhouses. The environmental control system of greenhouses cannot monitor mushroom growth. Thus, the control of microclimate is not for mushroom growth but for farmers’ feelings or experiences. To develop an intelligent system for monitoring mushroom growth, an artificial intelligence algorithm based on digital image processing was proposed in this study to automatically locate mushrooms and calculate the pileus circle. Compared to the method in the literature, the low-cost image analysis algorithm was used to calculate the pileus circle in the method. The advantage of this method was using low-cost computers or embedded systems which greatly reduces the deployment cost of intelligent image systems and the utilization rate. In the proposed method, the Bayes classifier was used to separate the target from the background to improve the accuracy of the mushroom location. Then, the image preprocessing, Hough transform for circle and self-developed circle-based region matching algorithm were used to locate the mushroom and then determine the mushroom size based on the pileus circle found. In order to verify the effectiveness of the proposed method in terms of the localization accuracy of the mushroom pileus circle, the average accuracy of the proposed method was 87.0%, which was higher than that of the traditional Circle Hough Transform method by 60.7%. Moreover, its localization stability was superior to that of Circle Hough Transform and the average running time of a single image is 2.3 s. Based on the result, the effectiveness of the proposed method meets the practical requirements of mushroom cultivation.
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