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Mature Tomato Fruit Detection Algorithm Based on improved HSV and Watershed Algorithm

机译：基于改进HSV和流域算法的成熟番茄果实检测算法

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Tomato is well known fruit since it has many essential and beneficial nutrients like antioxidant, vitamin C and A for human daily diet. Tomato picking by hand is both labor and time consuming. Therefore, to overcome these issues, tomato needs to be picked up automatically with the help of harvesting robot. Recently automation of fruit harvesting gains great popularity. To guide the harvesting robot to pick up the fruit correctly, it is important to correctly detect and find the location of the red mature fruit. In this study, a new mature tomato detection algorithm based on the improved HSV (Hue, Saturation, Value) color space and the improved watershed segmentation was proposed, by using a cheap and easily accessible RGB camera for guiding a robot to pick up red tomatoes automatically. For that purpose, the color images of tomato plants were captured under natural light. At the first stage, improved HSV transform was used to remove background and to detect only red tomatoes. Then the morphological operations were applied to modify the detected fruit. The second step was the separation of connected fruits, so that the harvesting robot can pick up fruit correctly. To achieve this task, the improved watershed algorithm was applied. As a result, the detection of separated red mature tomato was done by using the improved HSV and watershed, with uneven illumination and complex background. The overall red fruit detection accuracy was up to 81.6%, and it shows good potential to be used on harvesting robot.
机译：番茄是众所周知的果实，因为它具有许多必不可少的抗氧化剂，维生素C和人类日常饮食的必备营养素。手工番茄挑选是劳动和耗时。因此，为了克服这些问题，番茄需要在收获机器人的帮助下自动拾取。最近果实收获的自动化越来越受欢迎。为了指导收获机器人正确拿起水果，重要的是正确地检测并找到红色成熟果实的位置。在本研究中，提出了一种基于改进的HSV（色调，饱和，值）颜色空间和改进的流域分割的新的成熟番茄检测算法，通过使用便宜且易于访问的RGB相机引导机器人拾取红番茄自动地。为此目的，在自然光下捕获番茄植物的彩色图像。在第一阶段，改进的HSV变换用于去除背景并仅检测红番茄。然后应用形态学操作来修饰检测到的果实。第二步是连接果实的分离，使收获机器人可以正确地拾取果实。为了实现这项任务，应用了改进的流域算法。结果，通过使用改进的HSV和流域进行分离的红色成熟番茄的检测，不均匀的照明和复杂的背景。整体红色水果检测精度高达81.6％，它显示出收获机器人的良好潜力。

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