Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

Abstract

Crop protection is a key activity for the sustainability and feasibility of agriculture in a current context of climate change, which is causing the destabilization of agricultural practices and an increase in the incidence of current or invasive pests, and a growing world population that requires guaranteeing the food supply chain and ensuring food security. In view of these events, this article provides a contextual review in six sections on the role of artificial intelligence (AI), machine learning (ML) and other emerging technologies to solve current and future challenges of crop protection. Over time, crop protection has progressed from a primitive agriculture 1.0 (Ag1.0) through various technological developments to reach a level of maturity closelyin line with Ag5.0 (section 1), which is characterized by successfully leveraging ML capacity and modern agricultural devices and machines that perceive, analyze and actuate following the main stages of precision crop protection (section 2). Section 3 presents a taxonomy of ML algorithms that support the development and implementation of precision crop protection, while section 4 analyses the scientific impact of ML on the basis of an extensive bibliometric study of >120 algorithms, outlining the most widely used ML and deep learning (DL) techniques currently applied in relevant case studies on the detection and control of crop diseases, weeds and plagues. Section 5 describes 39 emerging technologies in the fields of smart sensors and other advanced hardware devices, telecommunications, proximal and remote sensing, and AI-based robotics that will foreseeably lead the next generation of perception-based, decision-making and actuation systems for digitized, smart and real-time crop protection in a realistic Ag5.0. Finally, section 6 highlights the main conclusions and final remarks.

Keywords: artificial intelligence (AI); decision support system (DDS); deep learning; precision agriculture (PA); robotics; unmanned aerial vehicles (UAV).

Figure 1

The main stages of precision crop protection.

Figure 2

Taxonomy of machine learning according to the type of task to be solved.

Figure 3

Number of publications of CNN architectures commonly used in the three domains of precision crop protection (crop diseases, weeds and crop plagues) from 2010 to 2022 (source: Scopus). Figure compiled with the conjunction of “CNN architecture” and each of the three crop protection domains (crop diseases, weeds and crop plagues) as search criteria within the article title, abstract and keywords.

Figure 4

Publications trends (2010 – 2022) of traditional ML algorithms (colored solid areas) and ANNs (dashed red line) in all disciplines (A), and for precision crop protection applications (B), according to the proposed taxonomy (source: Scopus).

Figure 5

Multidisciplinary technological domain of Ag5.0 with a different degree of maturity and use ranging from mature technologies in the core circle to future technologies in the peripheral circle. CPU, central processing unit; GPU, graphics processing unit; TPU, tensor processing unit; DRAM, dynamic random-access memory; RDNA, radeon DNA; NVMe, non-volatile memory express; ASIC, application specific integrated circuit; FPGA, field programable gate array; LPWAN, low power wide area network; WLAN, wireless local area network; WPAN, wireless personal area network; WSN, wireless sensor network; IoT, internet of things; IIoT, industrial IoT; Ag-IoT, agricultural IoT; LiFi, light fidelity; WiMAX, worldwide interoperability for microwave access; TSN, time-sensitive networking; xG, cellular network generation.