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Developing a multi-label tinyML machine learning model for an active and optimized greenhouse microclimate control from multivariate sensed data

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Artificial Intelligence in Agriculture 2022年第1期6卷 129-137页

作者： Ilham Ihoume Rachid Tadili Nora Arbaoui Mohamed Benchrifa Ahmed Idrissi Mohamed Daoudi Solar Energy and Environment Laboratory Mohammed V University in RabatFaculty of SciencesB.P 1014RabatMorocco

In the uncertainties within which the worldwide food security lies nowadays,the agricultural industry is raising a crucial need for being equipped with the state-of-the-art technologies for a more efficient,climate-resilient and sustainable *** traditional production methods have to be revisited,and opportunities should be given for the innovative solutions henceforth brought by big data analytics,cloud computing and internet of things(IoT).In this context,we develop an optimized tinyML-oriented model for an active machine learningbased greenhouse microclimate management to be integrated in an on-field *** design an experimental strawberry greenhouse from which we collect multivariate climate data through installed *** obtained values'combinations are labeled according to a five-action multi-label control strategy,then used to prepare a machine learning-ready *** dataset is used to train and five-fold cross-validate 90 Multi-Layer Perceptrons(MLPs)with varied hyperparameters to select the most performant–yet optimized–model instance for the addressed *** multi-label control approach enables designing highly scalable models with reduced computational complexity,comprising only n control neurons instead of(1+∑n k=1Cn k)neurons(usually generated from a classic single-label approach from n input variables).Our final selected model incorporates 2 hidden layers with 7 and 8 neurons respectively and 151 parameters;it scored a mean accuracy of 97%during the cross-validation phase,then 96%on our supplementary test *** model enables an intelligent and autonomous greenhouse management with the less required *** can be efficiently deployed in microcontrollers within real world operating conditions.

关键词： Agricultural greenhouse microclimate control Machine learning Optimization TinyML

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Greenhouse microclimate environment adaptive control based on a wireless sensor network

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International Journal of Agricultural and Biological Engineering 2020年第3期13卷 64-69页

作者： Lina Wang Binrui Wang College of Mechanical and Electronic Engineering China Jiliang UniversityHangzhou 310018China

Environmental parameter measurements from various sensors spatially distributed in greenhouse crops can be used to create an accurate and detailed depiction of the climate in different regions of the *** environments have an impact on crop yield,productivity,quantitative and qualitative characteristics of plants,and various plant *** this study,an automatic monitoring system based on a wireless sensor network was designed to monitor the survival rate of greenhouse *** system design includes a sensor model selection and placement process,host computer monitoring software and communication module *** Zigbee protocol was used for wireless communication between the *** proposed fuzzy-PID controller was easy to design and highly adaptive to the measurement errors of the *** test results show that the system valuable for monitoring the environment in the greenhouse,where it was successful in controlling and maintaining an optimal microclimate condition.

关键词： greenhouse microclimate control wireless sensor network fuzzy-PID control

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