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 97% 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.
Cisotto, G., Tegegn, D., Zancanaro, A., Reguzzoni, I., Lotti, E., Manzoni, S., et al. (2024). An AI-empowered energy-efficient portable NIRS solution for precision agriculture: A pilot study on a citrus fruit. In Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS) (pp.313-318). Institute of Electrical and Electronics Engineers Inc. [10.15439/2024F5111].
An AI-empowered energy-efficient portable NIRS solution for precision agriculture: A pilot study on a citrus fruit
Cisotto G.;Tegegn D. D.;Manzoni S. L.;Zoppis I. F.
2024
Abstract
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 97% 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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