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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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