| 基于像素块和机器学习的播前棉田地表残膜覆盖率检测 |
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| 引用本文： | 翟志强,陈学庚,邱发松,孟庆建,王海渊,张若宇.基于像素块和机器学习的播前棉田地表残膜覆盖率检测[J].农业工程学报,2022,38(6):140-147. |
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| 作者姓名： | 翟志强 陈学庚 邱发松 孟庆建 王海渊 张若宇 |
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| 作者单位： | 石河子大学机械电气工程学院,石河子 832003,农业农村部西北农业装备重点实验室,石河子 832003 |
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| 基金项目： | 国家自然科学基金资助项目（32060412）；兵团中青年科技创新领军人才计划项目（2018CB016）；石河子大学高层次人才科研启动项目（CJXZ202104） |
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| 摘 要： | 由于农用地膜的长期使用，棉田残留地膜造成了严重的耕地环境污染。为了快速准确地检测播前棉田地表残膜污染，该研究提出一种基于像素块和机器学习的播前棉田地表残膜覆盖率检测方法。将原始图像通过不同尺寸分割的方法得到像素块，提取像素块的一阶、二阶、三阶颜色矩和灰度共生矩阵（Gray-level Co-occurrence Matrix，GLCM）纹理特征，通过像素占比判别方法提取标签。采用随机森林（Random Forests，RF）、极端梯度提升（Xtreme Gradient Boosting，XGBoost）、支持向量机（Support Vector Machine，SVM）和人工神经网络（Artificial Neural Network，ANN）4种算法构建残膜识别模型，计算棉田地表的残膜覆盖率。结果表明，20×20像素块下采用人工神经网络算法，残膜覆盖率检测值与真实值的相对误差最小，为0.51%，检测时间最短，为0.29 s。相比于像素点，像素块识别方法减小了样本数量，增加了像素点之间的相互特征，可快速准确检测残膜覆盖率，对农田残膜污染监测具有一定借鉴意义。
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| 关 键 词： | 机器学习 污染 像素块 残膜 棉田 检测 |
| 收稿时间： | 2021/11/20 0:00:00 |
| 修稿时间： | 2022/2/11 0:00:00 |
Detecting surface residual film coverage rate in pre-sowing cotton fields using pixel block and machine learning |
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| Zhai Zhiqiang,Chen Xuegeng,Qiu Fasong,Meng Qingjian,Wang Haiyuan,Zhang Ruoyu.Detecting surface residual film coverage rate in pre-sowing cotton fields using pixel block and machine learning[J].Transactions of the Chinese Society of Agricultural Engineering,2022,38(6):140-147. |
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| Authors: | Zhai Zhiqiang Chen Xuegeng Qiu Fasong Meng Qingjian Wang Haiyuan Zhang Ruoyu |
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| Institution: | 1. College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China; 2. Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi 832003, China |
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| Abstract: | Abstract: Residual film pollution has posed a great threat to the agricultural environment in recent years. It is a high demand for residual film identification and coverage rate detection in cotton fields before sowing. In this study, an evaluation method of residual film coverage rate was proposed using pixel block and machine learning model, in order to effectively recognize the residual film in the cotton field before sowing by the pixel block classification. Fifty images of 1 m×1 m cotton field surface with the residual film were collected by random sampling in the cotton growing area of Kuitun, Xinjiang, China. The image with a resolution of 4 608×3 456 (Pixel) was cropped along the boundary of 1 m×1 m sampling area. After cropping, the image was resized to 1 000×1 000 (Pixel), and the brightness was corrected using normalization. The image pixel was then manually labelled, in which the residual film was labelled as 1, and the soil background was labelled as 0. Then, 45 images were randomly selected to train the models, and the rest 5 images were used to verify the final model for the evaluation of residual film coverage rate. The recognizing pixel blocks and machine learning were selected to make better use of the color and texture features of images. Each image was cut into the 10 000, 2 500 and 625 pixel blocks, according to the sizes of 10×10, 20×20 and 40×40 (Pixel), respectively. The extraction was performed on the first, second, and third order color moments of R, G and B channels, and Gray-level Co-occurrence Matrix (GLCM) of each pixel block. Meanwhile, the intensities of R, G and B channels of each pixel were extracted for comparison. The stochastic down-sampling and Synthetic Minority Oversampling Technique (SMOTE) were used to equalize the pixel block data. Principal Component Analysis was employed to extract the top 10 principal components of pixel block, in order to prevent the over-fitting for the high training speed. Consequently, 70% of the data was used for the training, and 30% was for the testing. Random Forests (RF), Xtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Artificial Neural Network (ANN) were used to optimize the parameters via the style search and cross validation. The residual film coverage rate was calculated to evaluate the segmentation of different sizes of pixel blocks and the machine learning models. The ANN model combined with 20×20 (pixel) blocks performed the best, with the Mean Intersection Over Union (MIOU) of 71.25%. The relative error was 0.51% in the residual film coverage rate between the prediction and actual value, and the detection time was 0.29 s. Therefore, the improved model is feasible for the accurate identification of residual film on the surface of the cotton field before sowing. This finding can provide theoretical support for the rapid detection system of residual film pollution using UAV imaging. |
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| Keywords: | machine learning pollution pixel block residual film cotton field detection |
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