Editorial: Machine learning and artificial intelligence for smart agriculture, volume II

Abstract

Editorial on the Research TopicMachine learning and artificial intelligence for smart agriculture, volume II 1 Introduction Currently, AI is being widely used in various agricultural scenarios, including intelligent perception, real-time field monitoring, intelligent early warning, disease and pest detection, and intelligent decision-making for crop production environments. With the help of AI, farmers can now detect whether there are any diseases and pests, whether they need to use pesticides, and whether their plant protection practices are effective. This special edition focuses on several issues that still require further study and discussion, such as agricultural unmanned aerial vehicles, crop type mapping, crop phenotypic analysis, and the identification of crop diseases and pests in sustainable and intelligent phytoprotection. Agricultural unmanned aerial vehicleAgricultural unmanned aerial vehicles (AUAVs) integrate robots, AI, big data, and the Internet of Things. They have been widely applied to various agricultural operations, such as seed sowing, land monitoring, crop disease and pest detection, and pesticide and fertilizer spraying. AUAVs greatly improve agricultural production efficiency and liberate the labor force (Kim et al., 2019). They are becoming a new force in the field of precision agricultural aviation (Wang et al., 2019). Compared to traditional agricultural machinery, they are small, lightweight, and easy to transport, and have flexible flight control. AUAVs are characterized by precision operation, high efficiency, environmental friendliness, intelligence, and ease of use. However, in many cases, real-time changes in the AUAV load during flight can affect its speed, accuracy, and flight path stability. Xu et al. (Wei et al.) proposed a flight dynamics model for achieving AUAV flight trajectory stability using a PID controller and robust T-S fuzzy control method. This model can achieve certain stability in the flight path against load perturbations for different Frontiers in Plant Science frontiersin.org 01

Integration of Technology in Agricultural Practices towards Agricultural Sustainability: A Case Study of Greece

“…Since ECa is sensitive to variations in soil texture and salinity, the soil apparent electrical conductivity (ECa) sensors continually gather data on the field surface. Optoelectronic, acoustic, impedance, and nanostructured biosensors are used to identify soil insects and pests [63].…”