An AI-empowered energy-efficient portable NIRS solution for precision agriculture: A pilot study on a citrus fruit

Smart agriculture has seen impressive progresses in monitoring the quality of the crop and early detecting the onset of pathogens. However, this is typically achieved through smart, expensive, and energy-demanding robots and autonomous systems. We propose an AI-empowered portable low-cost short-wave near-infrared spectroscopy (sw-NIRS) solution that allows non-destructive measurements from plants and vegetables. In this pilot study, we specifically targeted an orange fruit and showed that it is possible to classify its different parts through sw-NIRS in the range 1350-2150 nm by using AI models, exceeding 98\% accuracy. Also, we explored the minimum amount of energy needed to reach such high classification performance. In the future, we aim to extend this investigation to other targets (e.g., bean plants), to develop AI architectures to more accurately model the physiological conditions of the target, and to create a network of sw-NIRS sensors to simultaneously monitor a large-scale crop.

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1. This work was partially supported by the MUR under the grant “Dipartimenti di Eccellenza 2023-2027" of the Department of Informatics, Systems and Communication of the University of Milano-Bicocca, Italy. AZ is also supported by PON 2014-2020 action IV.4 and GC by action IV.6 funded by the MUR.

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2. Thematic Sessions: Regular Papers

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3. Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).