Physics-Informed Neural Network for Daily Canopy Size Forecasting in Strawberry Production Using Fused Weather and Image Embeddings

Abstract

Abstract Purpose The study aimed to develop a hybrid Physics-Informed Neural Network (PINN) architecture to forecast the daily strawberry canopy volume using weather data and image-derived inputs. Green pixel counts were used as a low-cost proxy for canopy volume and tracked canopy growth in two strawberry cultivars under real-world field conditions. The PINN architecture was chosen because it directly embeds known growth constraints into the learning process, enabling reliable predictions even with a few observations per day. Methods Two commercially important strawberry ( Fragaria × ananassa Duch.) cultivars, ‘Florida Brilliance’ and ‘FL 16.30–128’ (marketed as Florida Medallion™) were used in this study. Strawberry plant images were acquired at 15-minute intervals over 38 days and were used to train the PINN architecture along with 13 weather data points and accumulated Growing Degree Days (GDD). The PINN was composed of a Long Short-Term Memory (LSTM) residual encoder and a trainable logistic growth curve driven by GDD. Biologically implausible canopy decline during GDD summation increases was penalized by the loss function. Results The Brilliance plant model yielded a Mean Absolute Error (MAE) of 6,996 pixels of canopy size and a Mean Absolute Percentage Error (MAPE) of 4.53%, while the Medallion plant model produced an MAE of 9,440 pixels and an MAPE of 16.4%. Both models outperformed a naive persistence baseline, which produced MAE values of 9,416 and 9,580 pixels for Brilliance and Medallion, respectively, and a physics-only logistic baseline, which yielded an MAE of 10,260 and 10,880 pixels for Brilliance and Medallion, respectively. Conclusion Combining a straightforward growth physics prior with a short-sequence LSTM significantly improved canopy forecasting accuracy, even with about a month of training instances. The system could be used to investigate strawberry growth during the vegetative and early fruiting periods to improve management and increase profit.

Code and data availability

The paper's strawberry canopy image dataset, green-pixel measurements, weather data, and PINN code are not publicly deposited; the authors state the data are available only on request. The FAWN weather network is an external data source, not a paper-specific asset.

No evidence-backed public reproduction asset is currently recorded.