| Titre : | From data to harvest: artificial intelligence and machine learning technologies revolutionizing agrophotovoltaic systems (2026) |
| Auteurs : | S. Ranganathan ; M. Ghamari ; M. Ramasamy ; S. Sundaram |
| Type de document : | Article |
| Dans : | Engineering Agriculture (vol. 13, n. 4, August 2026) |
| Article en page(s) : | p. 26675 |
| Langues : | Anglais |
| Langues du résumé : | Anglais |
| Catégories : |
Catégories principales 16 - TRANSPORT. INFRASTRUCTURE. ENERGIE ; 16.3 - EnergieThésaurus IAMM ENERGIE SOLAIRE ; INTELLIGENCE ARTIFICIELLE ; RENDEMENT DES CULTURES |
| Mots-clés: | AGRIVOLTAISME |
| Résumé : | Agrophotovoltaics (APV) integrates solar photovoltaic systems with agricultural production, offering a sustainable solution to meet growing energy and food demands. This review explores the transformative role of artificial intelligence (AI) and machine learning (ML) in optimizing APV performance. Key applications include energy yield forecasting, predictive maintenance, fault detection and crop-energy balancing strategies. Advanced algorithms, such as neural networks, decision trees and reinforcement learning, deliver high prediction accuracy and significant operational improvements. Reported studies show forecasting accuracies up to R2 of 0.96 for simulated irradiance datasets, representing model prediction performance rather than actual plant energy conversion efficiency. Likewise, AI-driven co-optimization of light distribution and irrigation improved leafy-vegetable yields by 10%-18% in experimental APV plots in India and France. The integration of Internet of Things sensors with ML models enables real-time environmental monitoring and dynamic resource management. This review presents the integrated synthesis of AI/ML applications specific to APV systems. It proposes a taxonomy of AI/ML use-cases, a research roadmap, and a quantitative synthesis linking model performance metrics with agronomic and energy outcomes. The study also identifies research gaps in data interoperability, environmental variability, model interpretability, and socioeconomic adoption. Addressing these challenges through interdisciplinary research and policy frameworks can accelerate intelligent APV deployment, advancing renewable energy, sustainable agriculture and climate resilience. |
| Cote : | En ligne |
| URL / DOI : | https://doi.org/10.15302/J-FASE-2026675 |
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