A Prediction Model for Double-Spikes Phalaenopsis Orchid

This study created a "Phalaenopsis double-spikes rate prediction model." We use AI object detection technology to combine machine learning models to extract the leaf spectrum of hyperspectral images of orchid seedlings and match them with the horticultural traits of orchid plants. This Know-how has significantly improved the traditional assay method.The application scenario is through the prediction model system developed by our team. For a particular batch of orchid seedlings, input its data, and then output the probability value of the double-spike rates of the whole bunch of orchid plants, sorted from large to small, and take the proportion of the shipment to get the evaluation result. The experimental results show that only 62.17% of the double spikes are correct if the traditional method is used for screening. Still, if this method is used for evaluation, choosing the top 10% of the plants with high scores and 97.50% accuracy of the orchid plants will produce double spikes. If the top 50% of the plants with high scores, 84.63% accuracy, will have double stalks. The proposed method can be proven in experiment results to effectively help flower farmers evaluate and select multi-floral stalk plants, improve the economic value of Phalaenopsis, and increase the competitiveness of Taiwan's export Phalaenopsis industry.

Co-researchers:Chen Y-W, Ko S-S*, Liu K-H, Chiu L-J, Lin Y-J, Lin FY-S, Hsiao C-H*