About this Research Topic

Background

The crop production is affected significantly by weeds due to their competition for light, water, nutrition and growth space, etc. As the major weed control method, the application of herbicides contributes greatly to inhibiting the growth of weeds both in arable fields and orchards. However, the chemical weed control strategy also has drawbacks. For instance, the drift and runoff of herbicides would lead to crop injury, agricultural non-point source pollution, and biodiversity loss. Furthermore, the frequently reported herbicide resistance problems are making the chemical control strategies more complex and less effective. Thus, integrated systems including mechanical weeding, rotation, variable spraying, or allelopathy inhibition need to be developed to improve weed control strategies. Meanwhile, in order to employ precise weed control strategies, there is also a demand to identify the weed individuals and their biophysical properties. This would significantly enhance the weed control efficiency, as a customized control approach can optimize the suppression of each weed species due to its individual characteristics.

Sensing technology is a key approach to generating precise spatial and temporal information about weeds in the fields. Various types of sensors combined with UAV or ground-based platforms have been used for the awareness of the species, density, and growth stages of weeds. Besides, sensor techniques are also efficient tools for the recognition, monitoring, and evaluation of stresses on weeds or crops. These can be regarded as indicator indexes of weed stresses on crops, or sometimes can also be used to monitor weed response to herbicide treatments. On the other hand, artificial intelligent technology has brought us to a new era of machine learning. Its high recognition accuracy enables more precise weed identification using visual or spectral imaging technologies, which can be of great significance for the application capability of sensors and precision agricultural systems such as mechanical weed control, UAV spraying, and precise targeting boom application.

Advanced field management systems rely on both precise sensing techniques and accurate application equipment. Thus, attempts of robotic weed management can lead to free labor resources from the fields and enhance the control effect, as the unmanned system can work 24h/7days so that it can carry on more applications within the proper weed control schedule.

This Research Topic will include cutting-edge research on the development and implementation of weed identification and integrated control. Papers are requested to address the latest developments for a wide range of tasks related to precision weed management, including research and recent advances in the following areas related to weed science:

- Herbicide resistance identification using sensors
- Integrated weed management systems
- Mechanical weed management
- Robotic weed control
- Sensorwise weed infestation evaluation
- Using AI for weed identification
- Variable herbicide application strategies and devices

Keywords: artificial intelligence, Integrated weed management, Robotic Weeding, Weed control, Weed identification

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