| 基于区域亮度矫正的番茄成熟度定量分级方法 |
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| 引用本文： | 张钟莉莉, 何婷婷, 李志伟, 史凯丽, 刘长斌, 郑文刚. 基于区域亮度矫正的番茄成熟度定量分级方法[J]. 农业工程学报, 2023, 39(7): 195-204. DOI: 10.11975/j.issn.1002-6819.202211192 |
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| 作者姓名： | 张钟莉莉 何婷婷 李志伟 史凯丽 刘长斌 郑文刚 |
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| 作者单位： | 1.北京市农林科学院智能装备技术研究中心，北京 100097;2.农业农村部农业信息软硬件产品质量检测重点实验室，北京100097;3.山西农业大学农业工程学院，太谷 030801;4.北京市农林科学院信息技术研究中心，北京 100097 |
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| 基金项目： | 北京市科技计划项目（Z201100008020013）；北京市数字农业创新团队数字设施应用场景建设项目（BAIC10-2022-E02）；吴忠自治区级农业高新技术产业示范区建设专项项目（2022WZYQ0001） |
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| 摘 要： | 针对现有番茄成熟度分级标准不统一，泛化性有待提高等问题，该研究提出一种基于区域亮度矫正的果面红色着色区域提取的方法。采用R-G法增强番茄表面的红色区域，利用Otsu分割方法提取表面着色区域，判断各着色区域的轮廓树结构以计算着色区域面积占图像总面积的比例作为主要特征，构建多因子融合的随机森林模型以实现番茄成熟度的量化分级。同时，利用基于局部亮度均衡的图像快速修复方法以解决光照变化导致的番茄表面高亮度反射问题。结果表明：以番茄表面着色面积比成熟度评价指标的分级平均正确率为92.96%，相比传统颜色矩和颜色直方图作为评价指标时的分级准确率提高了6.53和20.6个百分点。高亮区域领域像素加权替代法可对番茄高亮区域亮度实现有效矫正，校正后的未熟、半熟和成熟番茄图像的果面着色区域面积占番茄图像总面积的比例较矫正前提高了0.06、0.15和0.11，分级精度分别提高了17.24、11.47和4.69个百分点。研究可为番茄成熟度的定量性分级提供决策基础。
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| 关 键 词： | 随机森林 像素 番茄成熟度 亮度矫正 红色着色区域提取 |
| 收稿时间： | 2022-11-22 |
| 修稿时间： | 2023-03-17 |
Quantitative grading method for tomato maturity using regional brightness correction |
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| ZHANG Zhonglili, HE Tingting, LI Zhiwei, SHI Kaili, LIU Changbin, ZHENG Wengang. Quantitative grading method for tomato maturity using regional brightness correction[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2023, 39(7): 195-204. DOI: 10.11975/j.issn.1002-6819.202211192 |
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| Authors: | ZHANG Zhonglili HE Tingting LI Zhiwei SHI Kaili LIU Changbin ZHENG Wengang |
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| Affiliation: | 1.Intelligent Equipment Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;2.Key Laboratory for Quality Testing of Software and Hardware Products on Agricultural Information, Ministry of Agriculture and Rural Affairs, Beijing 100097, China;3.Agricultural engineering College of Shanxi Agricultural University, Taigu, 030801, China;4.Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China |
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| Abstract: | It is necessary to improve the generalization of tomato maturity grading for better consistent criteria in modern agriculture. In this study, an extraction was proposed for red-colored regions from the fruit surfaces using region brightness correction. The R-G method was used to enhance the red areas on the tomato surface, and then the Otsu method was used to segment them. All the colored areas on the tomato surface were obtained to evaluate the relationship between the contours of each red region of tomato after segmentation by the contour tree structure. The proportion of the area of the red region to the total area of the image was calculated as the main feature of maturity grading. The surface red coloring area ratio, color moments, and color histograms were selected to perform the feature importance analysis, in order to screen out the color features with a significant impact on the maturity grading. The random forest model was used to determine and grade the different maturity stages of tomatoes. Among them, the image in-painting technique based on the fast marching method was used to reduce the high brightness reflection of surface that reduced by the illumination changes. The test results showed that the effect of the area ratio of fruit surface coloring areas on the tomato maturity grading was significantly greater than the color histogram and color moment. The coloring area ratio, G-component third order color moments, and G-component color histogram feature posed the greatest impact on the maturity grading, with importance scores of 0.293, 0.199, and 0.127, respectively. Among all color features, the tomato coloring area ratio as a classification index shared the highest classification accuracy, with an average classification accuracy of 92.96%, which was 6.53 and 20.6 percent point higher than the traditional color moment and color histogram indicators. The domain pixel-weighted sum performed an excellent correction effect on the brightness of the tomato-highlighted areas. After correction, the proportion of the fruit surface colored area of immature, slightly mature, and mature tomato images to the total area of the tomato images increased by 0.06, 0.15, and 0.11, respectively, compared with before correction. The classification accuracy was improved by 17.24, 11.47, and 4.69 percent point, respectively. The area extraction of tomato red areas with the image fast restoration can be used as the recommended extraction of tomato maturity grading. |
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| Keywords: | random forest pixel tomato maturity brightness correction red shaded area extraction |
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