Automatic detection of crop yield ripeness is a tedious task because of the presence of various intensities of color in crops. One of the solutions to this problem is the monitoring of those crops by performing segmentation operations. This operation can help to distinguish the ripe and non-ripe regions among the crop images. For this purpose, this study presents a new hybrid crop image segmentation method utilizing type-2 fuzzy set (T2FS), K-means clustering algorithm, and modified quantum optimization algorithm (MQOA). The proposed method fully utilizes the indispensable qualities of these three techniques by (a) using T2FS to represent each color component of crop images in terms of secondary memberships, (b) applying K-means clustering algorithm to extract the similar features from the set of type-2 entropy values obtained from the secondary memberships, and (c) exploiting MQOA to optimize the distance function used in K-means clustering algorithm to obtain the optimal clusters. The performance of the proposed method is assessed based on the experiments carried out on color images of cherry tomatoes. Evidence of experimental results suggests that the proposed method produces extremely effective segmented images relative to those well-known color image segmentation methods available in the literature of pattern recognition and computer vision domains.

由于农作物中存在各种强度的颜色，因此自动检测农作物的成熟度是一项繁琐的任务。解决此问题的方法之一是通过执行分段操作来监视那些作物。此操作可以帮助区分作物图像中的成熟区域和非成熟区域。为此，本研究提出了一种利用2型模糊集（T2FS），K均值聚类算法和改进的量子优化算法（MQOA）的新的混合作物图像分割方法。所提出的方法通过（a）使用T2FS以次要隶属度表示作物图像的每个颜色分量，（b）应用K充分利用了这三种技术的必不可少的品质。-means聚类算法从从次要成员资格获得的2类熵值集中提取相似特征，（c）利用MQOA优化K- means聚类算法中使用的距离函数以获得最佳聚类。基于对樱桃番茄彩色图像进行的实验，评估了所提出方法的性能。实验结果的证据表明，相对于模式识别和计算机视觉领域文献中提供的那些众所周知的彩色图像分割方法，该方法可产生极其有效的分割图像。