Plant Leaves Region Segmentation in Cluttered and Occluded Images Using Perceptual Color Space and KmeansDerived Threshold with Set Theory Presence of clutters and occluding objects within agricultural farm environments challenges accurate segmentation of plant leaves, a prerequisite for an effective machinevisionbased automation of agricultural tasks. In this paper, we propose a plant leaves segmentation method that can be integrated into visionbased robotic harvester and quality inspection systems. The proposed method combines the discriminatory power of colorbased technique with the simplicity and computational efficiency of thresholdbased technique. Clutters and occluding objects are eliminated by infinitesimal angular displacement of the threshold image, followed by the application of set theory. Performance evaluation shows that the proposed method demonstrate strong robust features and computational efficiency.

Review of MachineVisionBased Plant Detection Technologies for Robotic Weeding Controlling weeds with reduced reliance on herbicides is one of the main challenges to move toward a more sustainable agriculture. Robotic weeding is a thought to be a viable way to reduce the environmental loading of agrochemicals while keeping the operation efficiency high. One of the key technologies for performing robotic weeding is automatic detection of crops and weeds in fields. This paper presents an overview on various methods for detecting plants based on machine vision, mainly concentrating on two main challenges: dealing with changing light and cropweed discrimination. To overcome the first challenge, both physical and algorithmic methods have been proposed. Physical methods can result in a more cumbersome machine while algorithmic methods are less robust. For cropweed discrimination, deeplearningbased methods have shown obvious advantages over traditional methods based on handcrafted features. However, traditional methods still hold some merits that can be leveraged to deeplearningbased methods. With the fast development of hardware technologies, researchers should take full advantage of advanced hardware to ease the algorithm design. In the future, the identification of crops and weeds can be more accurate and finegrained with the support of online databases and computing resources based on the advances in artificial intelligence and communication technologies.