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001041666 1001_ $$0P:(DE-Juel1)207602$$aAgarwal, Avinash$$b0$$eCorresponding author$$ufzj
001041666 245__ $$aInfrared Thermography in Plant Factories: Solving Spatiotemporal Variations Via Machine Learning
001041666 260__ $$aWeinheim$$bWiley-VCH GmbH$$c2025
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001041666 520__ $$aInfrared thermography (IRT) for real-time stress detection in plant factories (PFs) remains largely unexplored. Hence, this study investigates the feasibility of implementing IRT in PFs, using machine learning (ML) to address the challenges in information processing. Herein, purple basil plantlets were subjected to root dehydration within a pilot-scale PF, and canopy temperature was monitored at regular intervals using a thermal camera. Subsequently, eight ML models using the ‘support vector machines’ algorithm were tested for stress detection. Our findings revealed that differences in canopy temperature due to microenvironmental variations led to inaccurate representation of stress. Nonetheless, binary classification models trained using plants at medial and high stress overcame this issue by identifying stressed samples with 81%–94% accuracy. However, although models trained with medially stressed samples performed well for all stress levels, models trained using highly stressed samples failed to identify medial stress reliably. Additionally, ternary and quaternary classification models were able to identify unstressed samples but could not distinguish between different levels of stress. Hence, binary classification models trained using medially stressed samples overcame spatiotemporal variations in canopy thermal profile most effectively and provided probabilistic estimates of plant stress within the PF most consistently.
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001041666 7001_ $$0P:(DE-HGF)0$$ade Jesus Colwell, Filipe$$b1
001041666 7001_ $$0P:(DE-HGF)0$$aDinnis, Rosalind$$b2
001041666 7001_ $$0P:(DE-HGF)0$$aCorrea Galvis, Viviana Andrea$$b3
001041666 7001_ $$0P:(DE-HGF)0$$aHill, Tom R.$$b4
001041666 7001_ $$0P:(DE-HGF)0$$aBoonham, Neil$$b5
001041666 7001_ $$0P:(DE-HGF)0$$aPrashar, Ankush$$b6$$eCorresponding author
001041666 773__ $$0PERI:(DE-600)3120036-9$$a10.1002/moda.70012$$gVol. 3, no. 1, p. e70012$$n1$$pe70012$$tModern agriculture$$v3$$x2751-4102$$y2025
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