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.

Deep localization model for intrarow crop detection in paddy field Abstract   Automated and precise rice plant localization is crucial for the mechanization of rice production, which can facilitate targeted spraying, sitespecific fertilization, and mechanized weeding etc. Existing approaches adopted thus far have mainly focused on interrow weed detection or rice seedling row detection. Nevertheless, techniques for intrarow individual rice plant positioning continue to face major challenges induced by the specific paddy field environments or complex morphology of rice plant. This paper proposed a new deep localization network for intrarow rice detection at the single plant level in a paddy field. This method designed a twostage model. The module in stage 1 identified potential locations containing rice plants in the entire image. The module in stage 2 predicted the confidence of rice plant identification and refined the corresponding box bounds. The twostage processing modules shared a deep backbone network for learning fullimage convolutional features and are combined into a unified framework to facilitate an endtoend training. In addition, we constructed a rice plant detection dataset and proposed a taskoriented evaluation method for performance verification of the algorithm. Experiment results showed the proposed deep model achieved a high localization accuracy of 93.22% and a high testing speed of 15 fps, verifying the effectiveness and efficiency of the method. Using this method, we can develop techniques for finerlevel agriculture production, such as spraying and weed control, to achieve healthy and economical rice yields.