In this study, we propose a sparse recurrent neural network model with regularization to identify the environmental factors contributing to strawberry growth evaluation. The learning data consisted of eight environmental variables, including carbon dioxide concentration and solar radiation, measured by observation devices in a greenhouse. The target data included shipment volume, quality, growth status, and the farmer’s intuitive evaluation. Data obtained during three periods between February and March 2025 were used in this study. Model training was performed using backpropagation through time with the mean squared error as the loss function. To induce sparsity, L1 and L2 regularization were applied, suppressing moderately influential weights and yielding a more interpretable model structure. Unlike conventional black-box models that rely on post-hoc explanation techniques, the proposed method constructs an intrinsically interpretable learning model in which the influence of each environmental variable is directly reflected in the learned network structure. Rather than improving prediction accuracy, this study aimed to clarify the dominant environmental factors through a transparent and structurally constrained learning framework. The results suggested that growth evaluation was influenced by the carbon dioxide concentration around observation device 1, atmospheric pressure measured across multiple devices, and other environmental variables. These findings demonstrate that important environmental factors in strawberry cultivation can be effectively visualized, supporting transparent AI-based analysis and practical decision-making in agricultural production.
Hisatsune et al. (Sun,) studied this question.