This review paper discusses the development trends of agricultural autonomous all-terrain vehicles (AATVs) from four cornerstones, such as (1) control strategy and algorithms, (2) sensors, (3) data communication tools and systems, and (4) controllers and actuators, based on 221 papers published in peer-reviewed journals for 1960–2023. The paper highlights a comparative analysis of commonly employed control methods and algorithms by highlighting their advantages and disadvantages. It gives comparative analyses of sensors, data communication tools, actuators, and hardware-embedded controllers. In recent years, many novel developments in AATVs have been made due to advancements in wireless and remote communication, high-speed data processors, sensors, computer vision, and broader applications of AI tools. Technical advancements in fully autonomous control of AATVs remain limited, requiring research into accurate estimation of terrain mechanics, identifying uncertainties, and making fast and accurate decisions, as well as utilizing wireless communication and edge cloud computing. Furthermore, most of the developments are at the research level and have many practical limitations due to terrain and weather conditions.

Reusing and recycling treated wastewater is a sustainable approach to meet the growing demand for clean water, ensuring its availability for both current and future generations. Wastewater can be treated in such advanced ways that it can be used for industrial operations, recharging groundwater, irrigation of fields, or even manufacturing drinkable water. This strategy meets growing water demand in water-scarce areas while protecting natural ecosystems. Treated wastewater is both a resource and a challenge. Though it may be nutrient-rich and can increase agricultural output while showing resource reuse and environmental conservation, high treatment costs, public acceptance, and contamination hazards limit its use. Proper treatment can reduce these hazards, safeguarding human health and the environment while enhancing its benefits, including a stable water supply, nutrient-rich irrigation, higher crop yields, economic development, and community resilience. On the one hand, inadequate treatment may lead to soil salinization, environmental degradation, and hazardous foods. Examining the dual benefits and risks of using treated wastewater for agricultural irrigation, this paper investigates the complexities of its use as a valuable resource and as a potential hazard. Modern treatment technologies are needed to address these difficulties and to ensure safe and sustainable use. If properly handled, treated wastewater reuse has enormous potential for reducing water scarcity and expanding sustainable agriculture as well as global food security.

Deep learning (DL) and computer vision applications in precision agriculture have great potential to identify and classify plant and vegetation species. This study presents the applicability of DL modeling with computer vision techniques to analyze the nutrient levels of hydroponically grown four lettuce cultivars (Lactuca sativa L.), namely Black Seed, Flandria, Rex, and Tacitus. Four different nutrient concentrations (0, 50, 200, 300 ppm nitrogen solutions) were prepared and utilized to grow these lettuce cultivars in the greenhouse. RGB images of lettuce leaves were captured. The results showed that the developed DL’s visual geometry group 16 (VGG16) and VGG19 architectures identified the nutrient levels of lettuces with 87.5 to 100% accuracy for four lettuce cultivars, respectively. Convolution neural network models were also implemented to identify the nutrient levels of the studied lettuces for comparison purposes. The developed modeling techniques can be applied not only to collect real-time nutrient data from other lettuce type cultivars grown in greenhouses but also in fields. Moreover, these modeling approaches can be applied for remote sensing purposes to various lettuce crops. To the best knowledge of the authors, this is a novel study applying the DL technique to determine the nutrient concentrations in lettuce cultivars.