Dataset for water and nitrogen deficit stress detection in soilless tomato crops based on spectral indices

The dataset for water and nitrogen deficit stress detection in soilless tomato crops based on spectral indices focuses on analysing data from four experiments to detect water and nitrogen deficits automatically using a classification tree methodology. The sheet "schedule" provides the scheduling information for the nitrogen and water treatments applied to the plants to create deficit conditions. It also includes a timetable and planning details, likely outlining the experimental procedures and timelines.The file integrates key aspects of the research process, from data collection to operational planning.
A sheet named "Data 4Exp." contains the main experimental results, organised into groups based on periods. A total of 186 data samples were randomly split into 80% for training and validation and 20% for testing, as presented in "Data 4Exp. test" sheet. The full description of the methodology followed is presented in the manuscript:
Elvanidi, A., Katsoulas, N., & Kittas, C. (2018). Automation for Water and Nitrogen Deficit Stress Detection in Soilless Tomato Crops Based on Spectral Indices. Horticulturae, 4(4), 47. https://doi.org/10.3390/horticulturae4040047