Agrivoltaics: Current Research, Emerging Applications, and Future Trends

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Technology".

Deadline for manuscript submissions: 10 September 2026 | Viewed by 776

Editors


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Guest Editor
Campus Mainburg, Technische Hochschule Deggendorf, Industriestraße 2, 84048 Mainburg, Germany
Interests: energy system; system modeling; energy transition; international energy research

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Guest Editor
Department of Renewable Energy, Environment and Sustainability Institute (ESI), University of Exeter, Penryn TR10 9FE, Cornwall, UK
Interests: building physics; thermal radiation; solar powered electric vehicle (EV); transparent building envelops; sensor technology; floating PV (Water based pv); Agri-PV (agrivoltaics)
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Special Issue Information

Dear Colleagues,

Agrivoltaics (Agri-PV) has emerged over the past decade as an innovative response to increasing competition between agricultural land use and renewable energy deployment. Originally proposed to enhance land-use efficiency by co-locating photovoltaic systems with crop production, Agri-PV has evolved from experimental pilot projects into a recognized strategy supporting climate-resilient farming systems and sustainable rural development. Beyond electricity generation, agrivoltaics directly influences crop physiology, microclimate conditions, soil processes, and farm management practices. Partial shading from PV modules alters photosynthetically active radiation, temperature, evapotranspiration, and water-use efficiency, thereby affecting crop yield, quality parameters, and stress resilience.

This Special Issue aims to provide a comprehensive overview of agrivoltaics with a stronger focus on agricultural performance and ecosystem responses. It seeks to bridge agronomy, crop science, soil ecology, livestock management, the trade-off between water–energy–food nexus and energy engineering, fostering interdisciplinary collaboration, and knowledge exchange from global case studies.

Cutting-edge research topics include crop physiological responses under partial shading, yield and quality assessment, soil health, and biodiversity impacts, livestock-integrated agrivoltaic systems, farm management adaptation, water–energy–food nexus integration, and techno-economic and policy frameworks for sustainable deployment.

We invite original research articles, reviews, field experiments, modeling studies, and policy-oriented contributions addressing agrivoltaics from local to global agricultural perspectives.

Prof. Dr. Kedar Mehta
Dr. Aritra Ghosh
Guest Editors

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Keywords

  • agrivoltaics
  • agri-photovoltaics
  • sustainable agriculture
  • renewable energy integration
  • smart farming
  • digitalization
  • water–energy–food nexus
  • techno-economic analysis
  • climate-resilient agriculture

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Published Papers (1 paper)

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Research

28 pages, 4397 KB  
Article
Signal-Image-Level Multimodal Fusion Network for Fault Diagnosis of Photovoltaic Panels in Solar Insecticidal Lamps
by Xinsheng Zhou, Xing Yang, Zhengjie Wang, Lei Shu, Kailiang Li, Tuoyu Yang, Lusheng Yuan and Tongjie Li
Agriculture 2026, 16(13), 1394; https://doi.org/10.3390/agriculture16131394 - 26 Jun 2026
Viewed by 284
Abstract
Solar insecticidal lamps are important physical control devices for green pest management, but faults in their photovoltaic power supply units can reduce trapping efficiency and shorten service life. To improve fault identification under complex agricultural environments, this study proposes a signal-image-level multimodal fusion [...] Read more.
Solar insecticidal lamps are important physical control devices for green pest management, but faults in their photovoltaic power supply units can reduce trapping efficiency and shorten service life. To improve fault identification under complex agricultural environments, this study proposes a signal-image-level multimodal fusion network (SIL-MMFN) for detecting and classifying photovoltaic panel operating states in solar insecticidal lamps. The method combines time-series measurements with short-time Fourier transform (STFT)-based time–frequency images. A convolutional image branch extracts spatial features from time–frequency representations, whereas a bidirectional GRU branch with attention models temporal dependencies in the original signals. In addition, physics-informed features based on the illumination–current residual and output power are introduced to enhance discriminative fault information. Field data collected from four agricultural deployment nodes were used to classify normal, open-circuit, and mismatch states. Experimental results show that the proposed method achieved an accuracy of 97.5%, precision of 96.7%, recall of 97.8%, and macro-F1 score of 97.3%, outperforming single-modality and representative comparison models. The results indicate that multimodal fusion helps reduce confusion between open-circuit and mismatch faults and provides a potential approach for operating-state monitoring and maintenance of agricultural photovoltaic equipment. In this study, fault diagnosis refers to the detection and classification of photovoltaic panel operating states, including normal, open-circuit, and mismatch conditions. Full article
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