Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art

Abstract

This article presents the current state-of-the-art research on applying artificial intelligence (AI) technology in smart greenhouses to optimize crop yields, water, and fertilizer use efficiency, to reduce pest and disease, and to enhance agricultural sustainability. The key technologies of interest were robotic systems for pesticide application, irrigation, harvesting, bio-inspired algorithms for the automation of greenhouse processes, energy management, machine path planning and operation of UAVs (unmanned aerial vehicles), resolution of scheduling problems, and image signal processing for pest and disease diagnosis. Additionally, the review investigated the cost benefits of various energy-management and AI-based energy-saving technologies, the integration of photovoltaics and dynamic pricing based on real-time and time-of-use metrics, and the cost benefits of LoRa, Wi-Fi, Bluetooth, ZigBee, mobile, and RFID (radiofrequency identification) technologies. The review established that commercially viable AI technologies for agriculture had increased exponentially. For example, AI-based irrigation and soil fertilizer application enabled farmers to realize higher returns on investment on fertilizer application and gross returns above the fertilizer cost, higher yields, and resource use efficiency. Similarly, AI image detection techniques led to the early diagnosis of powdery mildew. The precise operation of agricultural robots was supported by the integration of light imaging, detection, and ranging (LIDAR) optical and electro-optical cameras in place of the traditional GPS (geographic positioning systems) technologies, which are prone to errors. However, critical challenges remained unresolved, including cost, disparities between research and development (R&D) innovations and technology commercialization, energy use, the tradeoff between accuracy and computational speeds, and technology gaps between the Global North and South. In general, the value of this review is that it surveys the literature on the maturity level of various AI technologies in smart greenhouses and offers a state-of-the-art picture of how far the technologies have successfully been applied in agriculture and what can be done to optimize their usability.

Typology for Decision Support Systems in Integrated Pest Management and Its Implementation as a Web Application

“…By categorising IPM DSSs according to specific criteria such as target pests, crop types, and functions (i.e., monitoring, forecasting, planning), the typology enables users to efficiently find solutions tailored to their specific needs. This targeted approach to the DSS selection supports improved adoption, which can lead to reductions in pesticide use while maintaining yield quality and quantity [16,[18][19][20][21][22]24]. This has a positive impact on the profitability of agricultural production [73,74], the alleviation of direct and indirect impacts on soil organisms [75], and the population of natural enemies in and around crops [74].…”

“…DSSs are interactive, computer-based systems that assist decision makers in one or more decision-making processes and guide them to optimal solutions/next steps [14]. They improve the accuracy and efficacy of IPM decision-making [15][16][17][18][19][20][21][22][23][24] by using integrated decision rules, algorithms, and models in conjunction with one or more databases and user input data (e.g., crop or pest observations) [25,26]. Innovations in information technology (IT) and associated digital infrastructure are driving the rapid development of IPM DSSs [27], and evidence for the benefits of DSSs is well established [15][16][17][18][19][20][21][22]28].…”

Closed-loop agriculture systems meta-research using text mining

“…If, however, we're creating a closed-loop system where these pumps are dependent on various energy sources, and those energy sources are dependent on other resource availability and the repurposing of existing resources, then it is critical that there is experimental-based research that assesses these various aspects. The application of Internet-of-Things (Maraveas et al, 2022) and artificial intelligence (Maraveas, 2023) can potentially allow for the optimization and increased communication between various components of a closed-loop greenhouse. In addition, there are benefits concerning data collection in the system, allowing the closed-loop system to be more robust and resilient to factors such as climate change.…”

Navigating the future: exploring technological advancements and emerging trends in the sustainable ornamental industry

“…In the post-2000s, flower industry has undergone the most rapid and paradigm shift in harnessing technologies that have emerged as the world ushered in the 4.0 industrial revolution (Hossen et al, 2020). Today, growers have access to cutting-edge technologies that have revolutionized flower production (Maraveas, 2023). As a result, the ability to handle big data in recent years has allowed the floriculture industry to make use of the most significant technological advances like genetic engineering (Cardoso and Vendrame, 2022;Khan et al, 2022) to create new flower varieties with improved characteristics, such as longer shelf-life, unique colors, and fragrance.…”