Biosensing Technology in Agriculture and Biological Products

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Optical and Photonic Biosensors".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1489

Special Issue Editors


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Guest Editor
College of Engineering, China Agricultural University, Beijing 100083, China
Interests: smart urban agriculture; artificial intelligence; automated control; plant phenotyping; computer vision; crop plant signaling; machine (deep) learning; food processing and safety; fluorescence imaging; hyper/multispectral imaging; Vis/NIR/MIR imaging spectroscopy
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Guest Editor
Department of Plant Physiology, Faculty of Sciences, University of Granada, Granada, Spain
Interests: plant phenotyping; chlorophyll fluorescence; hyperspectral reflectance; thermography; plant stress detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Climate change poses a threat to the sustainable development of agriculture and biosystems. A great challenge for agricultural and biological science is to ensure that crop yields and bioproduct quality meet the demands of a growing population in the future. The interaction between genotypes and their environments controls the biophysical properties of crop plants, manifested in observable traits, i.e., plant phenotypes, which influence resource acquisition, performance, and yield. Moreover, the demand for the high quality and safety of agricultural products requires appropriate analytical tools to investigate them in order to achieve high standards of food quality and process control. With the rapid development of biosensing, fluorescence and spectroscopic techniques are becoming preferred tools for phenotypic analyses of crop plants, as well as for quality and safety analyses of bioproducts. In this Special Issue, we encourage the submission of original research articles and review articles on recent findings in advanced methods based on fluorescence and spectroscopy to assess complex plant traits, such as growth, development, tolerance, resistance, architecture, and physiology, as well as the safety and nutrients of postharvest agricultural and biological products to meet the planet's need for plentiful, nutritious, and flavorful product supplies.

Dr. Wen-Hao Su
Dr. María Luisa Pérez-Bueno
Guest Editors

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Published Papers (1 paper)

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Research

17 pages, 2093 KB  
Article
Plant Bioelectrical Signals for Environmental and Emotional State Classification
by Peter A. Gloor
Biosensors 2025, 15(11), 744; https://doi.org/10.3390/bios15110744 - 5 Nov 2025
Viewed by 929
Abstract
In this study, we present a pilot investigation using a single Purple Heart plant (Tradescantia pallida) to explore whether bioelectrical signals for dual-purpose classification tasks: environmental state detection and human emotion recognition. Using an AD8232 ECG sensor at 400 Hz sampling rate, we [...] Read more.
In this study, we present a pilot investigation using a single Purple Heart plant (Tradescantia pallida) to explore whether bioelectrical signals for dual-purpose classification tasks: environmental state detection and human emotion recognition. Using an AD8232 ECG sensor at 400 Hz sampling rate, we recorded 3 s bioelectrical signal segments with 1 s overlap, converting them to mel-spectrograms for ResNet18 CNN (Convolutional Neural Network) classification. For lamp on/off detection, we achieved 85.4% accuracy with balanced precision (0.85–0.86) and recall (0.84–0.86) metrics across 2767 spectrogram samples. For human emotion classification, our system achieved optimal performance at 73% accuracy with 1 s lag, distinguishing between happy and sad emotional states across 1619 samples. These results should be viewed as preliminary and exploratory, demonstrating feasibility rather than definitive evidence of plant-based emotion sensing. Replication across plants, days, and experimental sites will be essential to establish robustness. The current study is limited by a single-plant setup, modest sample size, and reliance on human face-tracking labels, which together preclude strong claims about generalizability. Full article
(This article belongs to the Special Issue Biosensing Technology in Agriculture and Biological Products)
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