We developed a simulator to quantify the effect of changes in environmental parameters on plant growth in precision farming. Our approach combines the processing of plant images with deep convolutional neural networks (CNN), growth curve modeling, and machine learning. As a result, our system is able to predict growth rates based on environmental variables, which opens the door for the development of versatile reinforcement learning agents

Albrecht, Patrick

Amacker, Julian

Grigore, Mihai

Horn, Claus

Kleiven, Thomas

ZHAW digitalcollection

A Learned Simulation Environment to Model PlantGrowth in Indoor FarmingJ. Amacker 1, T. Kleiven 2, M. Grigore 2, P. Albrecht 2, and C. Horn 11 Zurich University of Applied Sciences, ICLS, Schloss 1, 8820 Wädenswil, Switzerland2 Fruitful Farming AG, Höhenstrasse 11, 8954 Geroldswil, SwitzerlandAbstractWe developed a simulator to quantify the effect of changes in environmentalparameters on plant growth in precision farming. Our approach combines theprocessing of plant images with deep convolutional neural networks (CNN), growthcurve modeling, and machine learning. As a result, our system is able to predictgrowth rates based on environmental variables, which opens the door for thedevelopment of versatile reinforcement learning agents.Keywords: indoor farming, plant growth modeling, deep reinforcement learning.1 IntroductionPrecision farming technologies enable sustainable fresh food production and supply chain by allowingto control and steer the growth environment. Controlling and steering the environment makes itpossible to continuously optimize the growth path of every plant. However, depending on externalfactors, no one-size-fits-all growth recipes exist. Instead, there is a need to continuously monitorplant growth and adapt to the environment based on plant behavior.In this research, we monitor the environmental conditions using air and water quality (IoT) sensors,and capture behavior in reaction to changes in the environment. We focus on plants’ circadian rhythm,leaf area, plant growth, and plant color.Our main contributions are:• An end-to-end approach to plant growth modeling from seed to harvest;• An efficient hybrid curve-fitting and machine learning approach to quantify the effect ofenvironmental parameters on plant growth;• A demonstration of how model-based reinforcement learning (RL) can be enabled bylearning components of a simulator.The benefits of this approach translate to reduced operating costs, increased quality of the crop, andstable yield.2 MethodOur aim is to build a simulator that allows us to quantify the effect of changes in environmentalparameters on plant growth. This could then be used to understand and improve the growth process.In particular, such an approach provides the basis for the development of autonomous, intelligentagents to be deployed in indoor farms. Using these agents, one can dynamically optimize environmentparameters for every plant as a function of its current conditions. The corresponding modeling chainis illustrated in Fig. 1. It comprises four main steps, (1) image processing, (2) fitting of growth curves,Preprint. Under review.arXiv:2212.03155v1  [eess.SY]  6 Dec 2022Figure 1: Overview of our proposed plant growth modeling chain.(3) learning the dependence of growth parameters on environment parameters, and (4) constructionof the simulator. All steps are described in detail below.2.1 Recording and processing of plant growth dataDataset collection The data used in this experiment has been collected over a period of 12 monthsof growing multiple batches of basil. The collection of data consists of two parts:1. Environmental data (using IoT data);2. Top-down images of the crop (using cameras).In terms of (1) environmental data, the following variables were collected from the growth chamberevery 5 minutes:• CO2• Air temperature• Relative air humidity• Electrical Conductivity (EC)• Oxidation Reduction Potential (ORP)• Water pH• Water temperatureTo collect (2) top-down images of the crop, we used an 8-megapixel Raspberry Pi Camera Module 2.Crop images have been captured every 15 minutes. This low-cost camera module offers high-qualityimages and is suitable to deploy to both greenhouses and vertical farms to capture the growth ofcrops.Processing Upon collecting the dataset, we post-processed the images to quantify crop growth.Quantifying crop growth is done by estimating the size of the crop. This estimation is an objectrecognition problem consisting of two parts:• Crop localization; and• Crop classification.To be able to localize the crops, a potential approach is to remove the background using simplethresholding. However, as the environment does not remain identical (e.g., crop grows, dimming oflights), simple thresholding yields unsatisfactory results when compared to statistical models. Ideally,one needs to be able to accurately localize and classify the crop regardless of size and or unfavorableillumination conditions.In contrast to the status quo, our approach uses deep convolutional neural networks (CNN) applied toa less researched area – plant growth in controlled environment agriculture. The reasoning behind2Figure 2: Fitting of the growth curve example, showing the measured plant area per day (blue) andthe fitted curve (orange).our choice is that CNNs are considered state-of-the-art in image recognition [6] thanks to theirhigh learning capacity and robustness to the typical challenges of object recognition. To segmentand classify tens of thousands of top-down images of basil, we fine-tuned a CNN using transferlearning[2]. The result is an accurate method to quantify the crop’s size.2.2 Growth curvesTo quantify the plant growth over time, we use the green canopy cover (CC) as a measure of plantsize. Its development can be modeled with high average precision by the logistic growth curve [12]:CC(t) =CCmax1 + e−k(t−t0)(1)where CCmax is the maximum plant area, t0 the midpoint, with CC(t0) = CCmax/2, and k theplant growth rate.The data obtained from the image processing, i.e., the percentage area covered by plants, wassimplified by averaging daily measurements. The reason for averaging daily measurements is tosmooth out daily growth fluctuations due to light presence or absence. Individual batches were thenfitted using SciPy’s curve_fit function [13]. Lower and upper bounds were set to a fixed valueof 0 and 1, respectively. The values k and t0 were obtained from fitting the model. The fitting wasdone continuously for all batches with a growth period of D days and sub-batches starting from day5 (t = 5).In addition, the growth rates computed for all sub-batches can be used to iteratively estimate plantsizes at a future time step. This was only done for t+ 1 because the environmental parameters need tobe adjusted at t + 1. However, this adjustment can be extended further into the future. It is importantto note that the estimates at this time are based solely on past plant sizes and do not take into accountenvironmental factors.2.3 Dependence of growth parameter on environment parametersTo extend the simple growth model to a function reflecting growth dependencies on environmentparameters, a model was fitted using the growth rates k as target and environment parameters as inputvariables. Since the environment is the driving factor for plant growth, it is hypothesized that thesevariables can be used to predict k or the change in k for the next time step (t + 1).For interpretability purposes, multiple polynomial regression models were fitted to reflect dependen-cies of growth rates (k) on individual environment parameters. To find optimal polynomial degrees arange of values [1, 2, 3, 4] has been tested. To improve the models relative to the model trained over3Figure 3: Dependency of growth parameter k on water pH. Different colors correspond to differentplant batches.the entire dataset, we binned the data into different growth stages. That is, we grouped the data into 4bins of 25% of total area covered by plants. This suggests that optimal environmental factors changeduring the growth process. Figure 3 depicts such a dependency of k on the pH level of water in thesecond quarter (25% to 50% of the final plant size). Fastest growth is obtained for pH values between6.0 and 6.5, while smaller and especially higher values tend to slow down the growth process onaverage.2.4 Model trainingThe training set for the simulator-model consists of Basil batches. Given our initial dataset (30batches * 30 days), classic machine-learning methods were applied first. A complete list of testedmodels can be found in Table 1 below. The main input features consist of all environment variablesfrom the current day (dt), and the target variable, either k or ∆k, for the next day (dt+1). Otherfeature combinations, such as k ∗ dt and moving averages from two to six days, were included in thetest iterations.To account for different growth stages, e.g., early growth or saturation, two different binning methodsof the training data were tested in addition to training with the entire data set:1. 4 sequential bins containing 25% of the data, as described in Section 2.3.2. 15 bins containing 40% of the data with overlapping windows.These bins were restricted with a lower bound of 0.05 and an upper bound of 1. This leads to smallerbins at the beginning and the end of the growth process, loosely reflecting the S-shape of the growthcurve.The dataset was then split into 80% training data and 20% test data. The validation was done usingR2 and MSE[15]. The best model for the Basil data regarding the MSE was achieved using a linearregression model with overlapping bins for predicting k directly. The best model fit in terms of R2was achieved by a linear regression with sequential bins. The model for the simulator was chosen interms of the smallest MSE.2.5 Plant growth simulatorThe trained models for the various bins were implemented and tested in a simulation framework.Each growth batch was initialized with k = 0.2 and t0 = 20. The duration of the simulation was setto 35 days. At each step during the simulation, random environmental variables were drawn from anormal distribution with the mean and variance of each variable in the training set.4Figure 4: Workings of the prediction model. Past observations of environment parameter settings andplant growth are used as input to predict next day’s growth.Table 1: Performance comparison for different growth prediction model variantsModel Binning R2 MSEHuber Regressor overlapping 0.704392 0.008783Huber Regressor sequential 0.758954 0.007161Huber Regressor none 0.503647 0.005309Linear Regression overlapping 0.988588 0.000354Linear Regression sequential 0.989152 0.001132Linear Regression none 0.988169 0.000487Polynomial Regressor overlapping 0.733265 0.009973Polynomial Regressor sequential 0.792810 0.006454Polynomial Regressor none 0.629016 0.014098K-Nearest Neighbors overlapping 0.848751 0.004200K-Nearest Neighbors sequential 0.795600 0.005350K-Nearest Neighbors none 0.911697 0.003541Support Vector Machine overlapping 0.681001 0.006655Support Vector Machine sequential 0.638616 0.006822Support Vector Machine none 0.863949 0.003802During the simulation, the model of the bin whose center is closest to the current plant size is selectedfor the prediction of kt+1. The obtained prediction for kt+1 was then used to calculate ∆k bysubtracting the current value, i.e. the last prediction of k, from this prediction:∆k̂ = k̂t+1 − kt (2)Further, ∆k was normalized to accommodate for potential changes in the environment variablesduring the simulation. Since sudden changes in the environment may lead to unrealistically largepredictions of ∆k, the following normalization has been used:∆k̂norm = ∆k̂∆kmaxkmax(3)After adding ∆k̂norm to kt the next day plant size can be calculated by inserting the value into thegrowth curve (Eq. 1). Figure 5 depicts 10 repetitions and the final growth rate k of each simulatedbatch.5Figure 5: Example plant growth simulations for different environmental conditions.Figure 6: Comparison of rewards achieved by different strategies (higher is better). The given valuesare the mean episode rewards achieved in a test environment over 1000 episodes, where one episodecovers a full plant growth cycle from 3% to 95% of maximal plant area.To guide the system towards a desired target growth rate ktarget, we define the reward function as:R = −100 × (k − ktarget)2 (4)In our initial tries, we use the maximal value of k observed in the data as the target value ktarget =kmax.We compared the interaction with the simulation environment of three different strategies andobserved the rewards they achieve:• Random adjustments of plant environment parameters within the range of values observedin the data.• Applying the same adjustments that are present in the data.• Applying an AI-agent trained with PPO [11].The results are shown in Fig. 6. While the actions performed in the recorded data are certainlyfavorable to achieve faster plant growth we see a clear potential for AI agents trained in this way toimprove outcomes in indoor farming environments.63 Related WorkPlant growth models aim to describe the interaction between the growth of plants and their environ-ment [9]. Several types of plant growth models exist, with varying degrees of complexity, centeredon different key physiological processes and investigated at different temporal and spatial scales[5]. An alternative is to learn the input-output relationships directly from data with the help ofMachine Learning (ML), which has successfully been applied to plant growth prediction in the past.Conventional ML algorithms as well as deep learning were investigated, with LSTM [8] modelsgiving the best results [1]. However, these models only learn to predict the regular temporal patternof plant growth, without providing insights into the effect of environmental parameters[14]. Theusefulness of reinforcement learning to steer plant behavior has been recognized before and used inthe construction of several game-like simulation environments [7, 10]. Recently, RL solutions forsustainable agriculture have been proposed [3], and the first smart agriculture IoT systems basedon reinforcement learning have been constructed in China [4, 16]. However, to date, no ML modelexists which is able to actively control the complete growth process in an indoor farming setup, whiletaking into account growth speed, plant health and energy efficiency.4 ConclusionsWe developed a simulator for the automatic adjustment of environmental variables in indoor farming.We first fitted a growth curve model and subsequently trained a machine learning model to predictgrowth rates based on environmental variables. The resulting model could successfully be used in asimulator which enables the training of a reinforcement learning agent. Initial tests show potentialfor such agents to automate the control of plant environment parameters and optimize plant growth.Next, we plan to incorporate plant health objectives as well as energy efficiency objectives in thereward function and test our AI agent in a real-world setting.AcknowledgementsWe thank Innosuisse for the support within 59624.1 INNO-ICT.References[1] B. Alhnaity, S. Pearson, G. Leontidis, and S. Kollias. Using deep learning to predict plantgrowth and yield in greenhouse environments. Acta Horticulturae, 1(1296):425–432, November2020.[2] Nagender Aneja and Sandhya Aneja. Transfer Learning using CNN for Handwritten DevanagariCharacter Recognition. In 2019 1st International Conference on Advances in InformationTechnology (ICAIT), pages 293–296, July 2019. arXiv:1909.08774 [cs, eess].[3] Jonathan Binas, Leonie Luginbuehl, and Yoshua Bengio. Reinforcement Learning for Sustain-able Agriculture. CCAI workshop at the 36th International Conference on Machine Learning,page 3, 2019.[4] Fanyu Bu and Xin Wang. A smart agriculture iot system based on deep reinforcement learning.Future Gener. Comput. Syst., 99:500–507, 2019.[5] T. Fourcaud, X. Zhang, A. Stokes, H. Lambers, and C. Korner. Plant Growth Modelling andApplications: The Increasing Importance of Plant Architecture in Growth Models. Annals ofBotany, 101(8):1053–1063, August 2007.[6] Alexander Gomez, Augusto Salazar, and Francisco Vargas. Towards Automatic Wild Ani-mal Monitoring: Identification of Animal Species in Camera-trap Images using Very DeepConvolutional Neural Networks. Technical Report arXiv:1603.06169, arXiv, March 2016.arXiv:1603.06169 [cs] type: article.[7] Yasmeen Hitti, Ionelia Buzatu, Manuel Del Verme, Mark Lefsrud, Florian Golemo, and AudreyDurand. GrowSpace: Learning How to Shape Plants. 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AquaCrop: FAO’s crop water productivity andyield response model. Environmental Modelling & Software, 62:351–360, December 2014.[13] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, DavidCournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J.van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, AndrewR. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W.Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A.Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paulvan Mulbregt, and SciPy 1.0 Contributors. SciPy 1.0: Fundamental Algorithms for ScientificComputing in Python. Nature Methods, 17:261–272, 2020.[14] Chunying Wang, Weiting Pan, Xubin Song, Haixia Yu, Junke Zhu, Ping Liu, and Xiang Li.Predicting Plant Growth and Development Using Time-Series Images. 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A learned simulation environment to model plant growth in indoor farming

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A learned simulation environment to model plant growth in indoor farming

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