iGrow: A Smart Agriculture Solution to Autonomous Greenhouse Control

Abstract

Agriculture is the foundation of human civilization. However, the rapid increase and aging of the global population pose challenges on this cornerstone by demanding more healthy and fresh food. Internet of Things (IoT) technology makes modern autonomous greenhouse a viable and reliable engine of food production. However, the educated and skilled labor capable of overseeing high-tech greenhouses is scarce. Artificial intelligence (AI) and cloud computing technologies are promising solutions for precision control and high-efficiency production in such controlled environments. In this paper, we propose a smart agriculture solution, namely iGrow: (1) we use IoT and cloud computing technologies to measure, collect, and manage growing data, to support iteration of our decision-making AI module, which consists of an incremental model and an optimization algorithm; (2) we propose a three-stage incremental model based on accumulating data, enabling growers/central computers to schedule control strategies conveniently and at low cost; (3) we propose a model-based iterative optimization algorithm, which can dynamically optimize the greenhouse control strategy in real-time production. In the simulated experiment, evaluation results show the accuracy of our incremental model is comparable to an advanced tomato simulator, while our optimization algorithms can beat the champion of the 2nd Autonomous Greenhouse Challenge. Compelling results from the A/B test in real greenhouses demonstrate that our solution significantly increases production (commercially sellable fruits) (+10.15%) and net profit (+87.07%) with statistical significance compared to planting experts. The data and source codes of our work are provided as supplementary materials, and more details are available at: https://github.com/holmescao/SmartAgricultureSolution-iGrow.

A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture

“…These technologies can further automate the greenhouse processes and increase their precision [21]- [23]. For instance, AI can analyze the vast amounts of data collected, predicting future needs and helping make even more accurate adjustments [24], [25]. Computer Vision can monitor plant growth, detect diseases early, and even identify when crops are ready to be harvested [12], [26]- [29].…”

Smart and Efficient Greenhouses: A Systematic Literature Review in the Agronomic, Computational, and Energy Scope

“…This data was sent to the Microsoft Azure cloud and displayed in a smartphone app. The results showed the feasibility of integrating IoT in controlled environments, sending and receiving data from cloud computing platforms and good results in classifying the variables using Fuzzy logic techniques.The study byCao et al (2021) uses the Markov decision process to optimize the decision-making process performed by artificial intelligence (AI) algorithms. The article shows the efficiency of using AI techniques, such as reinforcement learning, heuristics, and Markov Decision Process (MDP), which is a way of modeling processes where transitions between states are probabilistic, it is possible to observe at what state the process is, and it is possible to interfere in the process periodically (in "decision times") by executing actions.…”