The aim of this study was to evaluate the potential of cloud computing technology to classify protected tomato plants under different irrigation treatments. Two tomato varieties, HeZuo 903 and WanShiRuYi, were used in sheltered cultivation for two seasons. Three water treatments were carried out (normal watering, no watering during the first fruit soaking period, and no watering during the two fruit soaking periods). The near infrared visible reflection spectra of the tomato canopies were collected during the fruiting season. Three sets of spectral data were used, including the original reflectance spectra, first derivative reflectance spectra, and absorption spectra.

The successive projection algorithm (SPA) was used to select data from six wavebands (483, 557, 674, 783, 869, and 964 nm) as optimal wavebands. The cloud computing platform was created using the Hadoop and Spark frameworks. The MLlib machine learning library from the Spark framework was used …

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