针对复杂多变的农田环境下,田间作物分割既要保留农田作物完整外部形态信息,又要满足农田作业速度的要求,该文提出一种基于反向变异粒子群优化(reverse mutation- particle swarm optimization,RM-PSO)算法提取最优颜色系数的田间作物分割方法.该分割方法分为离线和在线2个部分,离线部分采用反向变异策略提高了初始粒子群群体质量及算法的搜索效率,避免算法早熟收敛,陷入局部最优,引入满意度函数对最优颜色系数进行评价,提取全局最优颜色系数.在线部分采用离线提取的最优颜色系数对作物图像灰度化,进而对灰度化后图像进行阈值分割得到最终的分割结果.试验结果表明,该文方法平均错分率(error distinguish rate)仅为4.8%,低于HSI算法、EXG法以及Mean-shift神经网络分割算法的11.3%、19.5%、5.7%;标准差值为3.1%,相较于HSI算法的7.2%、EXG法的14.7%、及传统PSO方法的7.9%,该文算法具有更高的稳定性;平均处理时间为0.311 s,而HSI方法为0.908 s,Mean-shift神经网络分割算法为1.942 s.该方法不仅能够保证不同光照及不同景物干扰下作物外部形态信息完整,同时处理速度快,鲁棒性好,具有较高的实际应用价值.%Image segmentation algorithm can effectively extract target information, thus providing an important precondition for the application of intelligent agriculture. How to retain the external features of field crops as well as meet the requirements of farmland work speed in a complicated and changeable farmland environment is one of the important problems to be solved in the current farmland image segmentation algorithm. At present, color segmentation method is the most commonly used one for field crop image segmentation. There are obvious color feature differences between crops and soils, so the most intuitional color information of the image can be used for effectively extracting information of the crops. However, the commonly used color model can keep the shape of the crop but can't meet the requirements of speed and different illuminations at the same time. In recent years, machine learning methods have been applied in the crop image segmentation in some studies to improve the traditional segmentation algorithms. Though these methods can segment field image more precisely in different light conditions, the algorithm results rely on a data training process which needs a long calculation time, so it is difficult for these methods to be applied in real-time work. Therefore, this paper proposes a field crop image segmentation method based on reverse mutation - particle swarm optimize (RM-PSO) algorithm to solve the mentioned above problem. This method is divided into offline and online parts. The optimal coefficients of color are obtained by training data samples offline. The coefficients can be used to segment field image in real time. The method proposed in this paper can improve the process speed. The paper focuses on offline part and puts forward a strategy of RM to improve the PSO algorithm by training sample data. Firstly, the reverse particles are generated from the initial ones by using the reverse space strategy, and we compare the fitness of the reverse particles with that of the initial ones to eliminate the particles of poor quality and construct a new initial particle swarm, thus improving particle diversity and the quality of initial particles. Secondly, when the evolutionary particles are in a passive state during the process of particle iteration, we mutate the passive particles to avoid premature convergence of the algorithm and running into local optimum. Thirdly, we introduce a satisfaction function to evaluate the optimal color coefficient of a single sample in the color searching space, and take the global optimal color coefficient as the final result. As for the online part, the images collected in real time will be processed with the global optimal color coefficient, the between-class variance of the processed gray-level images will be calculated with the automatic threshold segmentation method, and then the maximum between-class variance threshold will be used as the optimal one to obtain the segmenting images. By comparing this method with frequently-used color segmentation methods, the experimental result shows that under different light conditions and interference in different sceneries, the method in this paper can make crops maintain complete external feature while ensuring the processing speed. According to the statistical data of 30 test images, for the algorithm segmentation accuracy, the method in this paper has an average wrong segmentation ratio of 4.8%, which is lower than the HSI (hue, saturation, intensity) algorithm (11.3%), the excessive green algorithm (19.5%) and traditional PSO (7.6%); for the algorithm stability, the algorithm in this paper has a wrong segmentation ratio standard deviation of 3.1%, which is lower than the HSI algorithm (7.2%), the excessive green algorithm (14.7%) and traditional PSO (7.9%), so it is of higher reliability; for the processing speed, the method in this paper takes nearly the same processing time as the excessive green algorithm and traditional PSO, namely 0.311, 0.303 and 0.319 s respectively. Therefore, the method in this paper provides a reliable technology basis for field crop identification and possesses high practical application value.
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