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•Unsupervised and supervised methods assessed the response of melon genotypes to NaCl.•Unsupervised statistical analysis made a valuable understanding of tendencies and grouping.•Supervised statistical analysis easily discriminates samples by genotype and salinity.•Multivariate and univariate statistics help choose genotypes with high salt tolerance.

Internet of Things (IoT) was employed for monitoring the growth parameters of rocket (Eruca sativa) plants cultivated in a greenhouse under supplemental LEDs lighting. Red (R), blue (B) and the combination red and blue in different ratios (75R:25B, 50R:50B, 25R:75B), were employed, as well as white LEDs. The LED lamps were automatically turned on at 18:00 and off at 24:00. Thus, the plants were grown in an 18 h photoperiod until the harvest time under daylight supplementary lighting (i.e. 12 h sunlight and 6 h LED). Yield, biomass, leaf area (LA), leaf area index (LAI), leaf area rate (LAR), leaf weight rate (LWR), specific leaf area (SLA), relative growth rate (RGR), crop growth rate (CGR) and net assimilation rate (NAR) of rocket plants were investigated, through sensors of temperature, humidity and light intensity via a Raspberry Pi board by the Message Queuing Telemetry Transport (MQTT) communication protocol. ANOVA results demonstrated that 50R:50B LEDs lighting produce higher yield, total biomass, LA, CGR, LAI, LWR, as well as lower SLA as compared to other treatments. Reliability analysis showed that IoT monitoring system is stable and reliable for controlling of environmental conditions (i.e., temperature, humidity) of the greenhouse.