Exploration of Autonomous Mobile Robots through Challenging Outdoor Environments for Natural Plant Recognition Using Deep Neural Network Modernization of living environments and human activities have severe effects on many parameters and factors such as climate change and global warming, an increase of incidence and the severity of wildfires, land surfaces and ice sheets, ecological imbalance, change of fertility of the soil, flow of energy, food security, etc. In addition, human modernization has had a negative impact on biodiversity and the natural environment. An integral component of modernization is agriculture that associates with the outdoor environment and relevant issues. Automation of agricultural activities contributes to reducing the dependency on human labor and the harmful effects on the natural environment. The correct identification of plants in outdoor environments has been neglected and many critical environmental and nonenvironmental limits and factors, such as weather conditions, time, viewpoint, lighting conditions (illuminations and light intensity), distance, etc., have not been considered in existing plant recognition systems and technologies. Hence, there is a demand to develop mobile and realtime systems for plant recognition in natural environments. This paper addresses these challenges and introduces the application of autonomous mobile robot and semirobots for recognition of natural plant species in outdoor environments. Furthermore, the proposed system presents the use of employing lowcost cameras, such as iPhone 6s, Canon EOS 600D and Samsung Galaxy Note 4, for plant recognition system in realtime. The performance of the system has been tested with a number of experiments in different years, 2017 and 2018, and at different times of day, morning and evening. The proposed system is a combination of new technologies involving deep learning concepts and an autonomous field robot to carry out precise plant recognition in challenging environments. The final accuracy of the mobile realtime system is 84.1666%.

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.