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.

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

In view of the poor quality of manual spray in greenhouse and easily causing personnel applying pesticide poisoning,a remote control system of self-propelled mist sprayer based on short message service was designed.This system set the walking speed and spraying flow of the mist sprayer by mobile short messages,and the mist sprayer could spray automatically controlled by micro control unit.The test results showed that the response time of the system was about three seconds and the maximum response distance was only 0.59 min the maximum walking speed of the mist sprayer.In the process of actual use,in order to obtain better spray uniformity,walking speed should be valued between 4-12cm/s and spraying flow should be valued between 50-300mL/min.The designed system could satisfy the requirement in greenhouse and compared with the traditional method,it greatly simplified the operation process,reduced labor intensity and safeguarded the personal safety.

Design and experiment of remote control system of greenhouse self-propelled mist sprayer

In mushroom greenhouses at IKM Simbang Maros, watering is currently done manually using a hose and scheduled twice daily, without considering the optimal temperature and humidity for mushroom growth. This study aims to develop an automatic watering system for oyster mushroom cultivation using a DHT22 sensor to monitor temperature and humidity. The system is equipped with a NodeMCU ESP32 microcontroller and a mist sprayer, triggered by a relay to activate the water pump. An IoT platform using Thingspeak allows farmers to monitor temperature and humidity data in real-time on their smartphones. In a real case study, the device effectively maintained optimal humidity levels for mushroom growth, with the pump activating when humidity was below 85%. Data from 10 observations showed that the pump was active for 7 times. Data processing using R Studio showed the system's ability to stabilize humidity levels. The proposed system could help farmers optimize mushroom growth and reduce the labor required for manual watering. 

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Automatic Mist Sprayer for Oyster Mushroom Cultivation Using Thingspeak IoT Platform and R Analytical Tool

Implementation of manual plant protection in a Chinese solar greenhouse (CSG) is expensive and labor-intensive. Moreover, this system cannot realize the separation of people and drugs. In a CSG, plants are cultivated at different distances, directions, and heights. Consequently, pesticide application via air-spraying is not uniform because the volume of pesticide droplets that deposit on plants is disproportionate. In this study, a CSG inter-row sprayer was designed to realize the automation of plant protection and the uniformity of pesticide application. With a double-track suspension, the sprayer could move freely along the east and west directions of the CSG and perform two spraying modes, namely, fixed boom spray and lifting spray. These modes could be applied depending on different crop growth stages and protection requirements. Electromagnetic detection and positioning technology were used to detect crop rows and satisfy inter-row spraying requirements. Automatic lifting and spraying operations based on crop height as detected by the sensor were realized by a lifting motor. The effects of inter-row lifting and fixed boom sprays on droplet deposition and penetrability were tested using mature tomatoes cultivated in the CSG. Results demonstrate that the average volumes of droplets deposited on the obverse and reverse sides of tomato leaves are 1.77 and 0.817 μL per square centimeter in the inter-row lifting spray, and the average variation coefficients of droplet volume deposition are 7.3% and 19.53%, respectively. In the fixed boom spray, the average volumes of droplets deposited on the obverse and reverse sides of tomato leaves are 1.12 and 0.086 μL per square centimeter, respectively, and the average variation coefficients of droplet volume deposition are 33.2% and 74.7%, respectively. These findings indicate that inter-row lifting spray significantly increases droplet deposition on the reverse side of tomato leaves and improves the uniformity and penetrability of droplet deposition. This study improves the automation degree of plant protection and uniformity of pesticide droplet deposition, as well as provides supplementary options for plant protection in CSGs.

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Guoying Shi

Papers

Design and Testing of an Inter-row Sprayer in a Chinese Solar Greenhouse