Agrivoltaics, the innovative convergence of photovoltaic (PV)

Open Access Journal: MDPI Energies

Fraunhofer ISE

Agrivoltaics, the innovative convergence of photovoltaic (PV) energy generation and agricultural activities, has garnered increasing scholarly and practical interest due to its potential to protect crops and promote sustainable energy production. PV greenhouses, in particular, have been instrumental in this context, offering a controlled environment for crop cultivation while simultaneously harnessing solar energy. However, the intricacies of PV system design have a pivotal role to play, as suboptimal configurations may inadvertently impede crop photosynthesis by reducing available sunlight. Our study looks closely at PV greenhouse design. We use computer simulations to explore various designs, like solar panel arrangements, greenhouse shapes, and orientations. Our findings support the use of checkerboard solar panel layouts and a north-south orientation for better light quality and quantity in the greenhouse.To validate the practical applicability of our research, we undertook the implementation of the designed PV greenhouse within the framework of the SusMedHouse project, located in Ankara, Turkey. This empirical endeavor encompassed a full agricultural cycle during the period of 2022-2023, thereby affording a tangible validation of our research findings.
In conclusion, the precision of design parameters emerges as a central element in the successful deployment of agrivoltaic systems, facilitating a harmonious interplay between enhanced agricultural productivity and sustainable energy generation. The SusMedHouse project in Ankara serves as an exemplar of research translation into pragmatic agricultural practices, demonstrating the potential for broader adoption of these innovative configurations.

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High technology against the effect of climate change on Mediterranean agriculture

EL MUNDO

WOLA

A Moncofa laboratory participates in the European project SusMedHouse with tools and systems that allow sustainable greenhouse production in the Mediterranean region. The Turkish city of Antalya has hosted the first face-to-face meeting of the institutions and companies participating in the R&D&I project SusMedHouse (Sustainability Competitiveness of Mediterranean Greenhouse and Intensive Horticulture) co-financed by the European Union. Seven partners from six countries, including the company Wola de Moncofa on behalf of Spain , are working on this project that promotes the European circular economy strategy, as it aims to innovate systems for the sustainability and competitiveness of the Mediterranean greenhouse. “SusMedHouse aims to achieve sustainable solutions and scientific and technological advances that promote possible future developments in the field of horticulture in the Mediterranean,” explains Wola CEO, Teófilo Díez-Caballero.

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Leveraging the capabilities of artificial intelligence (AI

AR&TeCS

AR&TeCS

Leveraging the capabilities of artificial intelligence (AI), this study seeks to advance agricultural practices by systematically analyzing the interconnections between environmental variables, plant growth indicators, and pest occurrences. Distinct findings and methodologies encompass:
Methodological Approaches: Deployed an array of machine learning algorithms, namely Random Forest, XGBoost, Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM), to scrutinize variables such as pesticide efficiency, presence of biological predators, and environmental factors including temperature, humidity, pH, EC, and PAR.
Inter-Variable Correlations: Unearthed pivotal relationships, notably the pronounced link between ambient temperature and pest prevalence. Further, nuances of plant growth determinants, including illumination (outdoor lux), soil pH, and ambient moisture, were examined in relation to external influences, with a marked emphasis on the pronounced role of bees during specific growth phases (Plant Growth Rates I, II, III).
Data Enhancement Techniques: To augment the analytical accuracy, mutual information was utilized to prioritize salient features, while log(x+1) transformations rectified skewed datasets, bolstering the robustness of statistical interpretations.
Advanced Imaging Protocols: The amalgamation of hyperspectral and infrared imaging, in tandem with CNNs trained on an expansive dataset of over 3,000 images, paved the way for precocious, non-intrusive pest detection. The adoption of tiling techniques further refined image segmentation, especially for pests spanning diverse sizes.
Modeling Outcomes: Remarkable precision was achieved in prognosticating pest outbreaks and plant growth trajectories. The endeavor was channeled towards curtailing the Mean Absolute Error (MAE) for ensemble models, and diminishing epoch-wise training losses for neural architectures.
Comparative Model Analysis: The versatility of Random Forest and XGBoost was manifest in their adept handling of mixed data types and capturing intricate, non-linear interdependencies. In contrast, the depth of neural models, especially RNNs & LSTMs, was underscored by their prowess in elucidating sophisticated relationships, albeit at the cost of augmented data and computational requisites.
Visual Analytics: Insightful graphical renditions elucidated the potency of pesticides vis-à-vis pest counts, and the sway of sowing cycles on pest and pathogen mitigation. Comparative analyses showcased the congruence between empirical findings and model-based predictions.
Keywords: advance agricultural practices, machine learning, Random Forest, XGBoost, Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM), mutual information, convitatianal neural networks (CNNs), Visual Analytics”