Research Progress of Intelligent Agricultural Technology and the Practice of Unmanned Farms

Dear Colleagues,

It is the era of artificial intelligence, which also applies to agriculture. With the progression of civilization, agricultural populations are decreasing dramatically in countries such as China and India, in addition to the USA and European countries. Quite a number of digital and unmanned farms have been brought into practice in order to explore the potential of intelligent farming technologies. In these unmanned farming practices, sensing technology and intelligent agricultural machinery are playing the most important roles in enhancing field management efficiency.

Sensing technology is a key approach to generating precise spatial and temporal information of crops in fields. Various types of sensors, combined with satellite-, UAV-, or ground-based platforms, have been used for the determination of nitrogen, phosphorus, and potassium contents in both soils and plants. Additionally, sensor techniques are also efficient tools for the recognition, monitoring, and evaluation of stresses on plants. These stresses can be water deficit, soil salinity, nutrition deficiency, high/low temperature, extreme radiation, wind, air pollution, and pests, including weeds, insects, and pathogens. On the other hand, artificial intelligence technology has brought us to a new era of machine learning; its high recognition accuracy enables more precise plant and pest identification, which can be of great significance for the application capability of sensors and precision agricultural systems such as mechanical weed control, UAV spraying, and precision fertilization.

Advanced field management systems rely on both precise sensing techniques and accurate application equipment. This being the case, attempts of robotic pest management or fertilizer application can lead to better growth uniformity of crops in fields; however, the mechanical management strategies may also bring stresses to crops. For instance, mechanical weeders may harm crops’ root systems, repeated crushing by wheels may damage the soil structure, pesticide drift may injure non-target crops in nearby fields, etc. Can these limitation factors in crop production be monitored by sensors mounted on application vehicles or UAVs? Machine–plant systems can be optimized by using real-time autonomous decision algorithms.

This Special Issue will include cutting-edge research on the development and implementation of the sensor identification of factors influencing plant growth, fertility, and pest management. Papers are requested that address the latest developments for a wide range of tasks related to precision agriculture, including research and recent advances in the following areas related to the field management of crops:

• Sensor-wise NPK evaluation and measurement.
• Herbicide stress recognition, monitoring, and evaluation of both weeds and plants.
• Stress or growth triggered by mechanical weed management.
• Plant phenotyping.
• Precision farming techniques and their effect on plant growth, fertility, and pest management.
• Using AI for plant and pest identification.
• Robotic pest management.

Dr. Pei Wang
Prof. Dr. Lijun Qi
Guest Editors

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• artificial intelligence
• phenotyping
• stress monitoring
• pest identification
• robotic pest management