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Open AccessArticle

A Phenotype-Based Approach for the Substrate Water Status Forecast of Greenhouse Netted Muskmelon

School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
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Author to whom correspondence should be addressed.
Sensors 2019, 19(12), 2673; https://doi.org/10.3390/s19122673
Received: 2 April 2019 / Revised: 5 June 2019 / Accepted: 6 June 2019 / Published: 13 June 2019
(This article belongs to the Special Issue Agricultural Sensing and Image Analysis)
Cultivation substrate water status is of great importance to the production of netted muskmelon (Cucumis melo L. var. reticulatus Naud.). A prediction model for the substrate water status would be beneficial in irrigation schedule guidance. In this study, the machine learning random forest model was used to forecast plant substrate water status given the phenotypic traits throughout the muskmelon growing season. Here, two varieties of netted muskmelon, “Wanglu” and “Arus”, were planted in a greenhouse under four substrate water treatments and their phenotypic traits were measured by taking the images within the visible and near-infrared spectrums, respectively. Results showed that a simplified model outperformed the original model in forecasting speed, while it only uses the top five most significant contribution traits. The forecast accuracy reached up to 77.60%, 94.37%, and 90.01% for seedling, vine elongation, and fruit growth stages, respectively. Combining the imaging phenotypic traits and machine learning technique would provide a robust forecast of water status around the plant root zones. View Full-Text
Keywords: muskmelon; phenotype; random forest algorithm; cultivation substrate water status; forecasting muskmelon; phenotype; random forest algorithm; cultivation substrate water status; forecasting
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Chang, L.; Yin, Y.; Xiang, J.; Liu, Q.; Li, D.; Huang, D. A Phenotype-Based Approach for the Substrate Water Status Forecast of Greenhouse Netted Muskmelon. Sensors 2019, 19, 2673.

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