LowCost Weed Identification System Using Drones Weeds compete with crops for resources such as light, nutrients, water and space. When mature, weeds can produce thousands to hundreds of thousands of seeds that can survive for a long time and posing a great threat to crops. The best way to avoid weed threats is to remove weeds before they bloom such that the chances of weed seeds falling into the soil can be reduced. Most existing dronebased weeds identification methods use additional equipments to enhance the identification ability. In addition to increasing the cost, such solutions also increase power consumption and load burden of drones. In this paper, we propose a lowcost Weed Identification System (WIS) using RGB images taken by drones as training data and applying Convolutional Neural Networks (CNN) to build the identification model. The result of the WIS can be used as a reference for agriculture researchers and can also be used to inform farmers to take necessary reactions. The WIS identifies weeds with an accuracy of up to 98.8%. Compared to other highcost methods, the WIS does achieve similar identification accuracy at low cost.

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.