Abstract—Orchid cultivation is a crucial aspect of the Philippines’ agricultural landscape, with over 900 indigenous species in the archipelago. However, the industry has declined due to various factors, including the destruction of natural habitats and limited cultivation efforts. This study investigates the impact of shade netting on the growth of Dendrobium Sampran orchids, employing an Artificial Neural Network (ANN) to analyze environmental variables such as temperature, humidity, and light intensity. The research aims to optimize growth conditions and enhance production quality using different shade net set-ups. The findings reveal that specific set-ups, particularly those using green shade nets at 50% and 75% shading, significantly improve growth metrics, including the number of buds, flowers, leaves, and plant height. The ANN model demonstrated predictive solid accuracy, with the lowest Mean Squared Error (MSE) values observed in these set-ups, indicating their effectiveness in fostering orchid development. Additionally, the study aligns with sustainable development goals by promoting responsible cultivation practices that minimize environmental impact and support biodiversity conservation. This research contributes to revitalizing the Philippine orchid industry and preserving its rich floral diversity by optimizing the growth environment through shade netting.  

Keywords—Artificial Neural Network (ANN), dendrobium, net, Orchids, shading

Cite: Pearl Princess Mae A. Abaya, Erna Lynn T. Dungo, and Nanette D. Santos, "AI-Driven Insights: Forecasting Dendrobium Sampran Growth with Neural Networks in Photoselective Shading," Journal of Advanced Agricultural Technologies, vol. 12, no. 1, pp. 15-22, 2025.

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