iGrow: A Smart Agriculture Solution to Autonomous ...

iGrow: A Smart Agriculture Solution to AutonomousGreenhouse Control

Xiaoyan Cao∗†

[email protected]

School of Informatics, Xiamen

University

Xiamen, China

Yao Yao∗

[email protected]

Tsinghua-Berkeley Shenzhen

Institute, Tsinghua University

Shenzhen, China

Lanqing Li

[email protected]

AI Lab, Tencent

Shenzhen, China

Wanpeng Zhang

[email protected]

Tsinghua University

Shenzhen, China

Zhicheng An

[email protected]



Shenzhen, China

Zhong Zhang

[email protected]

AI Lab, Tencent

Shenzhen, China

Shihui Guo

[email protected]

School of Informatics, Xiamen

University

Xiamen, China

Li Xiao

[email protected]



Shenzhen, China

Xiaoyu Cao

[email protected]

College of Chemistry and Chemical

Engineering, Xiamen University

Xiamen, China

Dijun Luo†

[email protected]

AI Lab, Tencent

Shenzhen, China

ABSTRACTAgriculture 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. How-

ever, the educated and skilled labor capable of overseeing high-tech

greenhouses is scarce. Artificial intelligence (AI) and cloud comput-

ing 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 incremen-

tal 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 re-

sults 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 Chal-

lenge. Compelling results from the A/B test in real greenhouses

demonstrate that our solution significantly increases production

∗Both authors contributed equally to this research.

†Xiaoyan Cao and Dijun Luo are the corresponding authors.

(commercially sellable fruits) (+10.15%) and net profit (+87.07%)

with statistical significance compared to planting experts. The data

and source code of our work are provided as supplementary mate-

rials.

KEYWORDSAutonomous Greenhouse Control; Incremental Model; Artificial In-

telligence Algorithm; Internet of Things; Cloud-Native Technology

1 INTRODUCTIONWith global challenges in food, energy, and water, the digital trans-

formation of the agricultural industry is extremely urgently in need.

In order to meet these challenges, smart agriculture solutions, based

on automation and intelligent decision-making, have attractedmore

and more attention[7, 17, 31].

Since governments have begun to launch development plans [42],

agricultural digitization has begun to take root globally. Modern

high-tech greenhouses equipped with sensors and actuators can

achieve a high level of autonomous control by integrating IoT and

edge computing technologies. With an efficient control strategy,

autonomous greenhouses ensure high crop production at a rela-

tively low resource cost. However, it would require experienced and

skilled labors to make optimal decisions based on the high volume

of digital information, not to mention the challenges to scale [6].

With the development of AI, new technologies are being applied

in the agricultural field [26]. To the best of our knowledge, there is

no mature AI-based solution for Autonomous Greenhouse Control

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arXiv preprint arXiv:3813763, July, 2021 Xiaoyan Cao and Yao Yao, et al.

(AGC). The planning algorithms based on forwarding search have

achieved remarkable success in the AI field [7]. However, these

planning algorithms all rely on the prior knowledge of environmen-

tal dynamics of the underlying system [44], such as game rules or

precise simulator dynamics. Therefore, with safety concern, they

cannot be directly applied to real-world applications, such as ro-

botics, industrial control, or controlled-environment agriculture.

Moreover, sample efficiency (high cost, long cycle, sparse reward)

has always been one of the main challenges of building data-driven

solutions in agriculture. A simulation model is able to generate vir-

tual trajectories and enables more efficient evaluation and iteration

of planting strategies. More importantly, it provides a testbed for

optimization of AI algorithms, which has the potential to take over

the decision-making process of AGC, securing reliable outcomes

while saving considerable labor.

In the past few decades, dynamic climate models [40, 43] and

crop models [15, 28] of greenhouse simulation have been devel-

oped. However, the existing models mainly rely on the domain

knowledge in physics and biology specific to greenhouse setups,

which is difficult to transfer to other scenarios. Nowadays, neural

networks (NN) and deep learning have replaced expert systems in

many fields [8, 18] by virtue of their generalization and expressive

power. However, the application of AI algorithms on greenhouse

simulation and control remains underexplored.

The quantity and quality of data are key to the success of data-

driven AI approaches in addressing the AGC problem. IoT technolo-

gies enable measurement of multimodal data [31], which can be

efficiently stored and processed via the cloud platform [22]. Thus,

the combination of IoT and the cloud provides an ideal solution for

agricultural data collection and management. However, limited by

the sensor capacity, only partial observations of the greenhouse

states can be obtained. Any AI models trained based on such col-

lected data inevitably lead to the simulation-to-reality gap [19]. By

incorporating an incremental scheme in our framework, the hope

is that by continuously accumulating sensor data, the proposed AI

algorithms can keep improving and eventually become deployable

in the large-scale production environment.

In this paper, we model the AGC problem and propose a smart

agriculture solution, namely iGrow, which is based on AI, IoT, and

cloud-native technologies, as shown in Figure 1. By autonomous

greenhouse control, iGrow has the potential of saving considerable

labor costs, thus significantly releasing the labor shortage problem.

We test our framework in both simulated and real greenhouses

scenarios. The experimental results demonstrate the technical and

economic value of our intelligent agricultural decision-making AI

module for the optimization of the AGC problem.

The main contributions of our work are summarized as follows:

• To our best knowledge, for the first time, the optimization

problem of autonomous greenhouse control (AGC) is formu-

lated as a Markov decision process (MDP) problem, which is

solved by our proposed bi-level optimization algorithm.

• We are the first to propose an NN-based three-stage simu-

lation model to approximate the transition dynamics of the

AGC problem, which is used as a testbed for the optimiza-

tion of control strategies. With a real agricultural dataset,

we validate that the accuracy of the NN-based simulator is

comparable to that of the state-of-the-art simulator based

on expert knowledge and rules.

• We propose a model-based iterative heuristic control algo-

rithm, with provably super-human performance in both sim-

ulation and reality.

• We test our solution in both simulated and real autonomous

greenhouses. Particularly, during the A/B test in real green-

houses, the experimental results demonstrate the perfor-

mance of iGrow solution statistically significantly (𝑆𝑖𝑔 <

0.01) exceeds that of planting experts with an average im-

provement of 10.15% in production and 87.07% in net profit.

2 RELATEDWORKDigital agriculture: IoT with cloud computing technology has

been used to provide smart agricultural solutions [31]. Some ap-

plications [27, 47] use wireless sensor networks for monitoring

crop fields. Thus, managers can monitor different environmental

parameters efficiently [9] via devices such as light, temperature,

relative humidity, and soil moisture sensors. These sensors are used

to measure and collect information, which will further be logged

and stored online using IoT and cloud computing [7]. Automated

irrigation systems [32] have been developed and used for the re-

mote control to minimize human involvement. However, to our

best knowledge, an end-to-end solution, such as closed-loop remote

control and decision-making system of autonomous greenhouses,

has not yet been fully developed and validated.

Greenhouse simulation: Simulation of a modern greenhouse

can be divided into two parts: modeling of the underlying (1)

physical dynamics (climate model) and (2) biological process (crop

model). There is a set of research focusing on the design of mech-

anistic greenhouse climate models to simulate physical dynamics

(such as temperature, CO2 concentration, humidity, etc.) in green-

houses [11, 33, 41, 45]. Integrating the first and second laws of

thermodynamics into the model helps improve simulation accu-

racy [11]. The nonlinear least square is the most used method

for parameter estimation [45]. The dynamic greenhouse climate

model has been used to combine model predictive control (MPC)

and evolutionary algorithm to solve a greenhouse open-loop op-

timization control problem [33, 41]. Crop modeling is the other

essential part of the greenhouse simulation, which can be used

to predict yield, growth, and nutrient relations of the greenhouse-

grown crops [28]. Researchers establish crop models by analyzing

crop growth mechanisms [4, 10, 24, 28]; for example, exploring the

relationship between leaf photosynthesis efficiency and dry mat-

ter [4]. Combining the crop model with the dynamic greenhouse

climate model enables growers to explore and optimize greenhouse

planting strategies [10]. As far as we know, AI approaches for mod-

eling greenhouse planting processes remain underexplored.

AI for agriculture: AI-powered agriculture has been studied

extensively since the turn of the millennium. Researchers have

tried various machine learning methods in agriculture, for which a

review can be found in [26]. Recently, deep learning techniques and

reinforcement learning (RL) techniques have also been applied in

agriculture. For deep learning in agricultural applications, [21] pro-

vides an excellent survey to start with, and applications with deep

learning include crop classification [25], crop disease detection [12],

2

iGrow: A Smart Agriculture Solution to Autonomous Greenhouse Control arXiv preprint arXiv:3813763, July, 2021

Cloud

Actuator

Sensor

DecisionMaking

Database

Visualization

IoT

Algorithm

Simulator

Greenhouse

HeatingIrrigation

Lighting

CO2 CO2

Figure 1: An overview of iGrow. Specifically, iGrow optimizes a strategy under an NN-based simulator, remotely deploys thestrategy to greenhouse, collects data via sensors, and then utilizes these data to update the current simulator incrementally.The overall procedure is in an iterative manner.

and remote crop count [35]. When it comes to RL and agriculture,

researchers focus on operational management (i.e., learning a good

decision-making sequence to maximize an objective). For exam-

ple, [14] studies wheat crop management with RL techniques. RL

has also been applied to resource consumption management. For in-

stance, [3, 5] used RL to optimize maize irrigation and water system

management in agriculture. However, few studies focus on optimiz-

ing automated greenhouse planting strategies via simulator-based

AI algorithms [1].

3 PROBLEM STATEMENTIn this work, we formulate the greenhouse control problem as a

stochastic MDP optimization problem. In the following section, we

give the necessary background of MDP and formally introduce the

problem of AGC under weather uncertainty.

3.1 MDP introductionAn MDP can be denoted by a tuple S = (S,A,P,R, 𝛾, 𝜌0), whereS ⊆ R𝑛 and A ⊆ R𝑚 represent the state and action spaces, re-

spectively. The dynamics or transition distribution are denoted as

P (𝑠 ′ | 𝑠, 𝑎), the initial state 𝑠0 ∈ S is assumed to follow the ini-

tial state distribution 𝜌0, R(𝑠, 𝑎, 𝑠 ′) gives the reward function of

executing action 𝑎 to transition from state 𝑠 to 𝑠 ′, and 𝛾 ∈ (0, 1]is the discount factor. A policy 𝜋 (𝑎 | 𝑠) on S maps a state 𝑠 to a

probability distribution over A that generates trajectories as 𝜏 =

(𝑠0, 𝑎0, 𝑟0, 𝑠1, 𝑎1, . . . , 𝑠𝑇 ), where 𝑎𝑡 ∼ 𝜋 (· | 𝑠𝑡 ), 𝑠𝑡+1 ∼ P (· | 𝑠𝑡 , 𝑎𝑡 ),𝑟𝑡 = R (𝑠𝑡 , 𝑎𝑡 , 𝑠𝑡+1), and 𝑇 is the terminate time step.

MDP optimization problem. Find a policy 𝜋∗ that maximizesthe cumulative expected return 𝑅𝑇 given 𝑠0:

𝜋∗ = argmax𝜋

𝐸𝜏 [𝑅𝑇 ] = argmax𝜋

𝐸𝜏

[𝑇−1∑︁𝑡=0

𝛾𝑡𝑟𝑡

]. (1)

3.2 Formulation of the AGC optimizationproblem

An autonomous greenhouse planting process can be denoted by a tu-

ple G =< 𝐶,𝐺, 𝑃,𝐴,𝑊 >, where 𝑐 ∈ 𝐶 ⊆ R𝑘1denotes greenhouse

climate,𝑔 ∈ 𝐺 ⊆ R𝑘2is the crop growth state, 𝑝 ∈ 𝑃 ⊆ R+∪{0} rep-

resents the production, 𝑎 ∈ 𝐴 ⊆ R𝑘3specifies the control setpoints,

and𝑤 ∈𝑊 ⊆ R𝑘4represents the outside weather.

The objective of AGC is to find a policy that both improves crop

yields and reduces expenses, such as resource consumption and

labor costs, and this can be viewed as a specific instance of the MDP

optimization problem. There are many factors involved in the AGC

optimization problem, but we can only obtain partial observations

from the greenhouse due to the limitation of the sensors. As a result,

we focus on several observable factors that have a significant impact

on crop cultivation, and omit other variables. Within this context,

we are able to specify the state space, the action space, the reward

function, and the transition function of the AGC MDP as follows.

• State space: In the AGC MDP, each state 𝑠 consists of a four

tuple < 𝑤, 𝑐, 𝑔, 𝑝 >, and details of each element are shown

in Table 1. It is noteworthy that each variable of the state is

a number with different units, which encode the different

information determining the growth status of the crop.

• Action space: Only four essential control variables are in-

volved in the AGCMDP, including temperature, CO2 concen-

tration, lighting, and irrigation. We present basic statistics

in Table 2. Notice that those action variables could affect the

state variables, except for the outside weather 𝑤 , which is

beyond control.

• Reward function: SinceAGC aims toweigh crop yields against

total expenses, we denote the cumulative return 𝑅𝑇 by the

crop gains minus the cost of control policy consisting of

resource consumption, labor costs and etc. By setting 𝛾 = 1,the reward function R can be converted into the formulation:

𝑟𝑡 = 𝑅𝑡+1 − 𝑅𝑡 .• Transition function: The transition function in AGC is as-

sumed to be unknown, so we seek for a model of the dynam-

ics P̂ (𝑠 ′ | 𝑠, 𝑎) as the approximation of P (𝑠 ′ | 𝑠, 𝑎) instead.

3.3 Bi-level optimizationLet \ be the parameterization of the predictive model P̂, and param-

eterize the control strategy 𝜋 by parameters 𝜙 . In AGC, P̂\ should

be learned before the optimization of strategy 𝜋𝜙 in consideration

of the sample inefficiency of crop planting. Within this context, the

3


Table 1: Basic state variables are defined in our model.

Category Name Description Unit

Outsideweather(𝑤 )

Iglob outside solar radiation 𝑊 /𝑚2

Tout outside temperature◦𝐶

RHout outside humidity %

CO2out outside CO2 concentration 𝑝𝑝𝑚

Windsp outside wind speed 𝑚/𝑠Tsky virtual sky temperature

◦𝐶

Indoorclimate(𝑐)

AirT indoor temperature◦𝐶

AirRH indoor humidity %

AirCO2 indoor CO2 concentration 𝑝𝑝𝑚

PAR photosynthetically active radiation 𝑢𝑚𝑜𝑙/𝑠Cropstate(𝑔)

LAI leaf area index -

PlantLoad number of growing fruits 𝑓 𝑟𝑢𝑖𝑡𝑠/𝑚2

NetGrowth photosynthesis net growth 𝑘𝑔𝐶𝐻2𝑂/𝑠Production (𝑝) FW fruit fresh weight 𝑘𝑔/𝑚2

Table 2: Basic actions are defined in our model.

Action (setpoint) Unitsindoor temperature

◦𝐶indoor CO2 concentration 𝑝𝑝𝑚

illumination on/off -

irrigation on/off -

AGC optimization can be formulated as:

max𝜙 𝑅𝑡 (\∗, 𝜙)s.t. \∗ = argmin\𝐿train (\ | 𝐷),

(2)

where L𝑡𝑟𝑎𝑖𝑛 (\ | 𝐷) is the training loss on a given dataset 𝐷 .

The above problem formulation is actually consistent with bi-level

optimization [46] in a broader scope since both 𝜙 and \ need to be

optimized in order to achieve better strategies. Basically,𝜙 and \ are

treated respectively as upper-level and lower-level variables that

are optimized in an interleaving way. In particular, the lower-level

optimization represents the simulator optimization with continuous

data collection, while the upper-level optimization corresponding

to the strategy iteration.

4 METHODOLOGYAs shown in Figure 1, we lay out our framework to the AGC opti-

mization problem. The proposed solution consists of several essen-

tial components and each of them plays a different role in bi-level

optimization. To be specific, the decision-making module firstly

leverages AI algorithms to optimize a strategy under a given simu-

lator, which actually is a predictive model learned from greenhouse

planting data in our context. Then, the strategy is deployed to the

greenhouse, supported by IoT and cloud technologies, and the cor-

responding planting records will be collected and stored in the

database. We recall that the continuous collection of new planting

data will undoubtedly contribute to the modification of the current

simulator, which is beneficial to the further iterative optimization

of strategy.

4.1 Incremental modelTo solve an MDP optimization problem, the state transition func-

tion P is required. However, the greenhouse planting process is

stochastic and dynamic, so it is reasonable to assume the P to be

unknown in the AGC optimization problem. To address this issue,

existing works simulate the process based on empirical formulas

including physics and biology domain knowledge [29, 43]. Com-

pared to traditional artificial models, the data-driven methods can

model the planting dynamic with less prior knowledge, as well as

perform with high accuracy and generalization [18]. Thus, we build

an NN-based three-stage simulation model to approximate P. Thedetailed modeling process is described below.

Greenhouse climate module: Crop growth is mainly deter-

mined by the indoor climate. Therefore, we need to build a model

to simulate greenhouse climate change. At time 𝑡 , indoor climate 𝑐𝑡is influenced by previous outside weather𝑤𝑡−1, control setpoints𝑎𝑡−1, and indoor climate 𝑐𝑡−1. Thereby, the indoor climate change

can be formalized as 𝑐𝑡 ← C(𝑤𝑡−1, 𝑎𝑡−1, 𝑐𝑡−1), where C repre-

sents the transition function of indoor climate change. Generalized,

the dynamic greenhouse climate change problem can be defined

by a tuple < 𝑊,𝐴,𝐶 >, and the climate change is mapped by

𝑊 ×𝐴×𝐶 → 𝐶 , where𝑊 ⊆ R6,𝐴 ⊆ R4 ,𝐶 ⊆ R4, (see Table 1 andTable 2 for details). We propose an NN-based structure to represent

model CΘ1, where Θ1 represents network parameters. The model

structure is shown in Figure 2. We adopt the mean-square error

(MSE) as the loss function: L (Θ1) = 1𝑁

∑𝑁𝑖=1

∑4𝑘=1

(𝑐(𝑘)𝑖− 𝑐 (𝑘)

𝑖

)2,

where L(Θ1) represents the average loss of 𝑁 samples and 𝑐(𝑘)𝑖

and 𝑐(𝑘)𝑖

represent real and prediction value of the 𝑘-th greenhouse

climate variable of the 𝑖-th sample, respectively. Additionally, we

assume greenhouse climate state transit once per hour, which is

considered to be accurate enough to approximate reality.

Growth state module: According to the greenhouse climate

model, we can obtain the indoor climate 𝑐𝑡−1 at time 𝑡 − 1, whichaffects current crop growth state 𝑔𝑡 . Their relationship can be ex-

pressed as 𝑔𝑡 ← G(𝑐𝑡−1, 𝑔𝑡−1), where G represents the transition

function of crop growth state. Furthermore, the problem of crop

growth can be defined by < 𝐶,𝐺 >, and the growth process is given

by 𝐶 × 𝐺 → 𝐺 , where 𝐶 ⊆ R4 and 𝐺 ⊆ R3. Similarly, as shown

in Figure 2, we build the state transition model GΘ2using NNs,

where Θ2 represents corresponding network parameters. Besides,

loss function and the frequency of state transition are similar to

greenhouse climate models.

Productionmodule: Production increase is infinitesimal within

one hour. Thus, we focus on daily production, which is more rea-

sonable. Production is determined by crop growth state. Therefore,

the process of production accumulation process can be generalized

as𝐺 ×𝑃 → 𝑃 , where𝐺 ⊆ R3 and 𝑃 ⊆ R+ ∪ {0}. However, the statetransition frequency of the growth state module is hourly. We ex-

tend 𝐺 to a vector ®𝐺 =

[𝐺 (0) ,𝐺 (1) , · · · ,𝐺 (23)

], where 𝑔

(𝑖)𝑑∈ 𝐺 (𝑖)

represents the growth state of the 𝑖-th hour of the day 𝑑 . Because

crop growth state is accumulated over time in our model, we only

need to consider 𝑔(23)𝑑

in the production module. Thus, production

of day 𝑑 can be denoted by 𝑝𝑑 ← P(𝑔(23)𝑑−1 , 𝑝𝑑−1), where P repre-

sents the transition function of production. Similarly, we use Θ3 as

4


𝐶𝑡

(a)

(𝑊𝑡−1, 𝐴𝑡−1, 𝐶𝑡−1) (𝐶𝑡−1, 𝐺𝑡−1) 𝐺𝑡 (𝐺 𝑡−1 +23, 𝑃𝑑−1) 𝑃𝑑

(b) (c)

Figure 2: Incremental Model Network Structure. (a) and(b) represent the greenhouse climate module and the cropgrowth module, respectively, which simulate the green-house planting process at the hour-level. (c) represents theproduction module, determined by crop growth state, andupdates once a day.

NNs parameters and the MSE as loss function to simulate produc-

tion accumulation process. The structure is shown in Figure 2 as

well.

Compared with one-module modeling, our model imposes con-

straints on the network structure and provides implicit regulariza-

tion, which reduces the demand for data and prevents overfitting.

Limited by sensor technology, we cannot obtain a full observa-

tion of the greenhouse planting process. Thus, the bias between

P̂ and P exists objectively. Theorem 1 [20] indicates that as long

as we improve the returns under the model 𝑅𝑇 [𝜋] by more than

𝐶 (𝜖𝑚, 𝜖𝜋 ), we can guarantee improvement in 𝑅𝑇 [𝜋].According to the central limit theorem [37], we can easily deduce

that lim|𝐷 |→+∞

P̂ = P and limP̂→P

𝐶 (𝜖𝑚, 𝜖𝜋 ) = 0, where |𝐷 | represents

the scale of the dataset. This inference manifests that model is able

to be accurate enough as long as enough real data. Therefore, we

introduce the concept of incremental model on the basis of the

three-stage model, that is, streaming update the model with the

newly collected data.

Theorem 1. Let the expected Kullback-Leibler (KL) divergencebetween the two transition distributions be bound at each time step by𝜖𝑚 and the policy divergence be bound by 𝜖𝜋 . Then, the real returnsand model returns of the policy are bound as:

𝑅𝑇 [𝜋] ≥ 𝑅𝑇 [𝜋] −[2𝛾𝑟max (𝜖𝑚 + 2𝜖𝜋 )

(1 − 𝛾)2+ 4𝑟max𝜖𝜋

(1 − 𝛾)

]︸︷︷︸

𝐶 (𝜖𝑚,𝜖𝜋 )

. (3)

4.2 Optimization algorithmsAI algorithms are known to be an efficient way to solve the MDP

problem [23], andwe consider two classical AI algorithms, including

a heuristic algorithm and a reinforcement learning algorithm, to

solve the AGC optimization problem.

Soft Actor-Critic: In this work, we adopt Soft Actor-Critic

(SAC) [16], a state-of-the-art off-policy reinforcement-learning al-

gorithm, to optimize the AGC strategy. SAC interacts with the

simulator constantly to collect transition tuples and store them.

SAC alternates between a soft policy evaluation and a soft policy

improvement. During soft policy evaluation, an NN-based soft Q-

function is updated by minimizing a soft Bellman residual. During

soft policy improvement, the policy is optimized by maximizing an

entropy objective. The optimization process is given in Algorithm 1.

Algorithm 1: Soft Actor-CriticInput: policy 𝜋𝜙 , Q-function 𝑄\ and replay buffer DOutput: Optimal 𝑠𝑡𝑟𝑎𝑡𝑒𝑔𝑦 after 𝑁 epochs

1 for 𝑁𝑒𝑝𝑜𝑐ℎ iterations do2 for each step 𝑡 do3 a𝑡 ← 𝜋𝜙 (s𝑡 )4 s𝑡+1, r𝑡+1 ← Simulation(s𝑡 , a𝑡 )5 D ← D ∪ (s𝑡 , a𝑡 , r𝑡 , s𝑡+1)6 for each gradient step do7 Sample random minibatch B ∼ D8 L𝑒 ← BellmanResidual(B)9 𝑄\ ←Minimize(L𝑒 )

10 L𝑖 ← KL-divergence(B)11 𝜋𝜙 ←Minimize(L𝑖 )

12 return 𝑠𝑡𝑟𝑎𝑡𝑒𝑔𝑦 𝜋𝜙

Algorithm 2: Elitist Genetic AlgorithmInput: Initial population size 𝑝_𝑠𝑖𝑧𝑒 , iteration limit 𝑖𝑡𝑟𝑙𝑖𝑚𝑖𝑡 ,

and the composition of polysomy 𝑝𝑜𝑙𝑦𝑠𝑜𝑚𝑦

Output: Optimal 𝑠𝑡𝑟𝑎𝑡𝑒𝑔𝑦 after 𝑖𝑡𝑟𝑙𝑖𝑚𝑖𝑡 iterations

1 𝑝𝑜𝑝 ← InitializePopulation(𝑝_𝑠𝑖𝑧𝑒)2 𝑠𝑡𝑟𝑎𝑡𝑒𝑔𝑦 ← DecodeGenetics(𝑝𝑜𝑝)3 𝑂𝑏 𝑗𝑉 ← Simulation(𝑠𝑡𝑟𝑎𝑡𝑒𝑔𝑦)4 𝑝𝑜𝑝.𝐹𝑖𝑡𝑛𝑉 ← FitnessEvaluation(𝑝𝑜𝑝, 𝑂𝑏 𝑗𝑉 )5 𝑝𝑎𝑟𝑒𝑛𝑡 ← 𝑝𝑜𝑝

6 for 𝑖𝑡𝑟𝑙𝑖𝑚𝑖𝑡 iterations do7 𝑜 𝑓 𝑓 𝑠𝑝𝑟𝑖𝑛𝑔← RouletteSelect(𝑝𝑎𝑟𝑒𝑛𝑡, 𝑝_𝑠𝑖𝑧𝑒)8 for each 𝑝𝑜𝑙𝑦𝑠𝑜𝑚𝑦 𝑐𝑖 do9 𝑜 𝑓 𝑓 𝑠𝑝𝑟𝑖𝑛𝑔← Crossover(𝑜 𝑓 𝑓 𝑠𝑝𝑟𝑖𝑛𝑔, 𝑐𝑖 )

10 𝑜 𝑓 𝑓 𝑠𝑝𝑟𝑖𝑛𝑔← Mutation(𝑜 𝑓 𝑓 𝑠𝑝𝑟𝑖𝑛𝑔, 𝑐𝑖 )11 𝑝𝑜𝑝 ← 𝑝𝑎𝑟𝑒𝑛𝑡 ∪ 𝑜 𝑓 𝑓 𝑠𝑝𝑟𝑖𝑛𝑔12 𝑠𝑡𝑟𝑎𝑡𝑒𝑔𝑦 ← DecodeGenetics(𝑝𝑜𝑝)13 𝑂𝑏 𝑗𝑉 ← Simulation(𝑠𝑡𝑟𝑎𝑡𝑒𝑔𝑦)14 𝑝𝑜𝑝.𝐹𝑖𝑡𝑛𝑉 ← FitnessEvaluation(𝑝𝑜𝑝, 𝑂𝑏 𝑗𝑣)15 𝑝𝑎𝑟𝑒𝑛𝑡 ← SelectTopSizeFit(𝑝𝑜𝑝, 𝑝_𝑠𝑖𝑧𝑒)16 𝐵𝑒𝑠𝑡𝐼𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙 ← SelectBestFit(𝑝𝑎𝑟𝑒𝑛𝑡)17 𝑠𝑡𝑟𝑎𝑡𝑒𝑔𝑦 ← DecodeGenetics(𝐵𝑒𝑠𝑡𝐼𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙)18 return 𝑠𝑡𝑟𝑎𝑡𝑒𝑔𝑦

Elitist Genetic Algorithm: Classical heuristic algorithms, such

as the genetic algorithm, simulate biological evolution to solve

deterministic optimization problems [34]. In this paper, we apply

an elitist genetic algorithm (EGA) [36], which can accelerate the

convergence of the AGC strategy. Because the state space of each

variable is different, we adopt multiple chromosomes to encode

these variables. That is, in one cycle, the same variables share the

same chromosomes by concatenation. The optimization procedure

is summarized in Algorithm 2.

5


In the actual planting process, we take the final accumulated net

profit as the objective. Therefore, we have to optimize the AGC

strategy throughout the whole planting period. The strategy re-

quires the weather forecast model 𝑝 (𝑤 |𝑡), which makes predictions

for future weather to support optimization.

4.3 Closed-loop iterative control of iGrowThe inherent model bias in the simulation can be exploited by the

optimization algorithms, leading to large reality gap between the

actual and simulated policy performance [20]. To alleviate this

problem, we propose an iterative scheme to enable continual but

potentially asynchronous evolution of both the model and the strat-

egy. As shown in Algorithm 3, for every 𝐾1 time steps, the control

strategy is updated on the current simulation model. The setpoints

produced by the latest strategy are fed to the real greenhouses

and new planting data are collected to expand the training dataset.

And for every 𝐾2 time steps, the simulation model will in turn

be updated by fine-tuning on the latest data buffer. The proposed

procedure resembles lifelong learning [13], with the flexibility of

updating the model and strategy at different paces to ensure stable

and optimal progression.

5 SIMULATION EXPERIMENTIn this section, we train the incremental model via real planting data.

Based on the model, we compare our AI algorithms with the logged

strategies of participants in anAutonomous Greenhouse Challenge1

organized by Wageningen University & Research (WUR).

5.1 DatasetWe use the dataset collected from the competition above. Five teams

used AI algorithms to control individual greenhouses to grow toma-

toes for 166 days remotely. A team of experienced growers also

participated in the competition as reference. This dataset contains a

comprehensive record of the sensing and control data used during

the competition, such as greenhouse temperature and CO2 levels.

However, only six trajectories are far from satisfactory for train-

ing a robust model. WUR developed a greenhouse tomato simulator

based on empirical formula to approximate the real greenhouse

planting process [17]. We randomly simulate thousands of control

strategies to record their rollouts, and collect these trajectories as

the training set of our model.

In the simulated experiment, the calculation for net profit is the

same as that of the Autonomous Greenhouse Challenge2.

5.2 Hardware, software and parametersThe hardware configuration is: Intel (R) Core (TM) i7-9700K, one

GeForce RTX 2080 Ti GPU, and the computingmemory is 12 GB.We

implement the model in a Python 3.6.13, PyTorch 1.7.1 environment.

In the training phase of neural networks, the batch size is 256, the

learning rate is set to be 1e-3, and the number of epoch is 150.

5.3 Simulation models comparisonIn our previous work [1], we have experimentally demonstrated

that the simulation accuracy of a data-driven NN-based model can

1https://www.wur.nl/en/project/autonomous-greenhouses-2nd-edition.htm

2https://www.kaggle.com/piantic/autonomous-greenhouse-challengeagc-2nd-2019

Algorithm 3: Closed-loop iterative control of iGrow

Input: Dataset 𝐷 , model P̂, the update period 𝐾1 of

strategy, and the update period 𝐾2 of model

Output: Updated model P̂ and updated dataset 𝐷

1 Initialize 𝜏 = ∅// A complete greenhouse planting period 𝑇

2 for 𝑡 ∈ [0,𝑇 ) do3 if 𝑡 𝑚𝑜𝑑 𝐾1 == 0 then4 𝜋 ← update 𝜋 by simulating from current 𝜏 on P̂5 if 𝑡 𝑚𝑜𝑑 𝐾2 == 0 then6 P̂ ← update P̂ by 𝜏

7 𝑎𝑡 ← 𝜋

// Perform action, then calculate reward and

collect observations in real greenhouse

8 𝑠𝑡+1 ← P(𝑠𝑡 , 𝑎𝑡 ), 𝑟𝑡 ← R(𝑠𝑡 , 𝑎𝑡 )// Log planting process

9 𝜏 ← 𝜏 ∪ (𝑠𝑡 , 𝑎𝑡 , 𝑟𝑡 , 𝑠𝑡+1)10 𝐷 ← 𝐷 ∪ 𝜏

gradually improve with the amount of training data. Therefore,

we randomly choose 1000 virtual planting trajectories generated

by the WUR simulator as the training set 𝐷1. Similarly, we con-

struct the testing set with 50 virtual planting trajectories. First,

based on the new dataset, we use the method in [1] to train a

baseline model. Next, to study the role of real data, we added real

planting data 𝐷 of 5 participants (except the champion team: Au-

tomatoes) to 𝐷1. To ensure the fairness of the comparison, we

randomly remove five trajectories from the training set, that is

𝐷2 = 𝐷1 − {𝜏1, 𝜏2, 𝜏3, 𝜏4, 𝜏5},𝑤ℎ𝑒𝑟𝑒 𝜏𝑖 ∼ 𝐷1. Accordingly, the train-

ing set of the incremental model is 𝐷 + 𝐷2. We use supervised

learning to train the incremental model. Specifically, we adopt the

oversampling technique [2] to address the dilemma of the imbal-

anced dataset. According to Table 3, the fitting accuracy of the

baseline and incremental models both are satisfactory. The overall

goodness-of-fit (𝑅2) of the baseline and incremental models are

0.911±0.009 and 0.906±0.046, respectively. From this point of view,

the baseline is slightly better than the incremental model.

However, the testing set, consisting of the virtual planting tra-

jectories generated by the WUR simulator, is naturally limited by

the inherent gap of simulation to reality. Therefore, the accuracy

of the baseline model maybe inferior to the incremental model in

the real scenario. To verify this conjecture, we input the planting

strategies of the champion team into different simulators to get

different virtual planting trajectories, and then compare them with

the real planting trajectory. Figure 3 indicates that the two models

we adopted both achieve comparable simulation accuracy to the

WUR simulator. Moreover, we observe that the incremental model

is more accurate than the baseline model, which demonstrates that

the real data is crucial in model calibration. This also shows that

lower-level optimization makes sense.

6

https://www.wur.nl/en/project/autonomous-greenhouses-2nd-edition.htm

https://www.kaggle.com/piantic/autonomous-greenhouse-challengeagc-2nd-2019


Figure 3: Comparing the accuracy of differentmodels onAu-tomatoes. (a) represents net profit simulated by the samestrategy on differentmodels. (b) represents the accumulatedabsolute error of different models compared to the ground-truth net profit.

Figure 4: Comparison of the three methods. (a) representsthe performance of different methods on our incrementalmodel. (b) and (c) show the 120-hour control strategies oftemperature and illumination provided by EGA and Au-tomatoes.

5.4 Comparison methods• Automatoes strategy: Automatoes is the champion of the

Autonomous Greenhouse Challenge. The organizer briefly

introduces their algorithm in [17]. They combine a predic-

tive control algorithm with expert knowledge. Since the

algorithm details are unknown, we directly conduct their

public setpoints on our incremental model.

• EGA: EGA is an improved genetic algorithm that improves

convergence by adopting an elite retention strategy.

• SAC: SAC is one of the current state-of-the-art off-policy

actor-critic reinforcement-learning algorithms for continu-

ous control problems.

According to Figure 4(a), we observe that: (1) The performance

of SAC is inferior to that of EGA. It is mainly because the delayed

reward is especially problematic in AGC, resulting in a severe chal-

lenge to RL algorithms. For example, current control setpoints may

Table 3: 𝑅2 of different variables in the incremental model,where, 𝑅2 ∈ [0, 1], represents the goodness of fit of the sim-ulation results of real observations, and closer to 1 meansbetter performance [30].

Method AirT AirRH AirCO2 PAR

Baseline 0.915 0.707 0.930 1.000

Incremental 0.986 0.941 0.988 1.000

Method LAI PlantLoad NetGrowth FW

Baseline 0.927 0.935 0.959 0.917

Incremental 0.669 0.849 0.939 0.880

not be reflected until a month later; (2) The Automatoes strategy

performs the worst simulation result, since the Automatoes inte-

grates prior knowledge for their decision-making, rather than using

a pure AI method. In this experiment, the access to our simulation

model is unlimited, enabling two AI algorithms to fully exploit

their optimization capabilities to obtain more fine-grained control

policies, for instance, to achieve hourly regulation as shown in

Figure 4(b).

6 PILOT EXPERIMENTIn the pilot phase, we use the model-based iterative EGA to optimize

an AGC strategy and combine IoT with cloud-native technologies

to deploy the smart agriculture solution, i.e., iGrow.

6.1 Deployment overviewWe chose tomatoes as the experimental crop in the pilot project

since they are one of the main greenhouse crops worldwide. Our

pilot program started in October 2019 and is still ongoing. Both

the control and experimental groups are required to manage the

tomato crops remotely in autonomous greenhouse compartments

to maximize net profit. As mentioned in Section 3.2, net profit is a

balance between gains and expenses. Gains are calculated accord-

ing to the production and the selling price, while total expenses

include resource consumption (electricity, heat, CO2, and water),

crop maintenance costs, and equipment amortization (by appor-

tioning the purchase price of equipment to six years). Additionally,

daily crop maintenance (crop management, fertilization and etc.)

of all compartments are equally treated by labors.

The control group’s compartments are managed by planting ex-

perts with growing experience of more than 20 years, while the

experimental group is controlled by iGrow, whose incremental

simulation model is not static but is updated monthly using the

recently collected planting data. Note that all compartments are

equipped with the same materials for a fair comparison. In ad-

dition, the control and the experimental groups need to control

the crop growth remotely according to the measured greenhouse

information monitored by the sensors.

6.2 Pilot project setupThe growers have to determine setpoints in advance, which will

be sent to the cloud platform through an application programming

interface (API) (the details of the controlled variables are shown

in Table 1). The cloud is responsible for generating instructions

7


for the actuators according to the currently measured data of the

greenhouse by sending setpoints every 10 minutes.

The deployed sensors are shown in Supplementary Figure S2.

These sensors continuously measure the data with frequency be-

tween every 1-3 seconds. Once the measured data is updated, it will

be transmitted to the cloud platform via an embedded sensor system.

Cloud-computing technology provides data cleaning, visualization,

and weather forecasts. Growers can monitor the entire process in

real-time through the graphical interface as shown in Supplemen-

tary Figure S3. Also, the measured data can be downloaded from

cloud storage.

As described in Section 4.3, we use IoT with cloud-native technol-

ogy to accumulate planting data. Furthermore, the decision-making

module of iGrow will optimize and update control strategies based

upon the incremental model on a daily basis. During the optimiza-

tion process, the previously predicted states of the simulation model

are continuously replaced by the real states recorded by the sensors

to calibrate the model errors and re-optimize the control strategy.

6.3 Case studyWe have been conducting 4 pilot experiments through some real au-

tonomous greenhouses in Liaoyang City, Liaoning Province, China

(See Supplementary Figure S1). iGrow solution has been deployed

in over 20 compartments, each of which covers an area of 1 Mu (1/15

hectare). In each pilot experiment, we all take A/B tests where the

control groups hire expert growers for greenhouse management,

whereas the experimental groups rely on the iGrow platform for

automated greenhouse control and management. The first phase

of the pilot project3ran from October 2019 to March 2020, and the

experimental group and the control group controlled 2 compart-

ments and 1 compartment, respectively. The experimental group

outperformed the control group by 4,000 RMB (≈500 euro) in net

profit on average per season. In the second phase, from March 2020

to July 2020, we scaled up the size to 2 and 5 compartments for

the control and experimental group, respectively. In this paper, we

utilize the results of the second phase for analysis and seek some

insights.

Note that we assume the results of the control group represent

the average level of growing experts, and we adopt an independent

sample T-test for the experimental group to verify the superiority

of iGrow. We recall that when the result of the T-test satisfies the

condition 𝑆𝑖𝑔 < 0.01, this indicates that the result is statisticallysignificant. Some key statistics are given as follows:

• Gains: The experimental group average increases by 26.30%

compared to the control group. It is because production and

price, which depends on the weight and the sweetness of

the fruits, improve statistically significantly.

• Costs: The average total costs of the control and experimen-

tal groups are 3,270.04 euro/Mu and 3,223.29 euro/Mu, re-

spectively. Although the experimental group consumes more

energy, the harvests from its compartments all finished one

week earlier than the control group (see Figure 5(a)), saving

daily crop maintenance costs.

• Net profit: Compared to the control group, the experimental

group shows increased gains and reduced costs, resulting

3https://www.tencent.com/en-us/articles/2201057.html

in an 87.07% improvement in net profit. See Figure 5 and

Table 4 for more details.

Figure 5: Comparison of the harvests between the control(controlled by growing experts) and the experimental (con-trolled by iGrow) group during the second phase of the pilotproject.

Table 4: Overall economic comparison during the secondphase of the pilot project.

Economic

Control

Group

Experimental

Group

𝑅𝐼∗ T-test

Energy Cost 13.90 ± 0.23 50.11 ± 20.48 -260.57% 2e-2

Crop Maintenance

Cost

1543.96 ± 0.00 1457.19 ± 5.1 5.62% 5e-6

Equipment

Emortization

1171.88 ± 0.00 1711.88 ± 0.00 0.00% -

Total Cost 3269.74 ± 0.23 3219.18 ± 18.14 1.55% 5e-3

Price 0.45 ± 0.00 0.49 ± 0.01 10.63% 5e-4

Production 16.64 ± 0.80 18.33 ± 0.68 10.15% 8e-3

Gains 4768.30 ± 9.79 6106.88 ± 189.09 28.07% 1e-4

Net Profit 1498.56 ± 10.03 2887.7 ± 183.45 92.70% 1e-4

*Relative Improvement: the percentage improvement of the experimental group

relative to the control group on the specified index.

We plot the pair relationship among four states (See complete

results from Supplementary Figure S4 to S13): AirT, AirCO2, Air-

PAR, and AirRH, which are the controlled variables under planting

experts and the iGrow platform. According to Figure 6, we observe

some useful insights: (1) The greenhouse controlled by the con-

trol group exceeds 30◦C more frequently, which is not suitable

for crop growth, sometimes even harmful to crop health [39]; (2)

Between 17-27◦C, photosynthetic enzymes are more active [38]. In

the meantime, the greenhouse controlled by the experimental group

shows higher CO2 concentration and PAR, contributing to enhanc-

ing photosynthetic intensity; (3) The greenhouse controlled by the

control group has more humidity above 70%, which is harmful to

crop health [38].

7 CONCLUSIONIn this paper, we formulate autonomous greenhouse control as a

stochastic MDP optimization problem and propose a smart agricul-

ture solution, namely iGrow. We build cloud IoT infrastructure to

collect and manage planting data. These data are used to update our

AI decision-making module consisting of a simulation model and a

8

https://www.tencent.com/en-us/articles/2201057.html


Figure 6: The relationship amongAirT,AirCO2, AirPAR, andAirRH under the control strategies.

control strategy. Both simulated and pilot results demonstrate the

superiority of our solution.

Our solution has potential to improve the automation level of

greenhouse climate control and boost growing efficiency in real

production environments. Moreover, our modeling methodology

provides a paradigm to build an accurate digital twin of the cor-

responding real-world applications so that AI algorithms can be

developed and tested without a real environment.

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