Automated IoT-Based Monitoring of Industrial Hemp in Greenhouses Using Open-Source Systems and Computer Vision

Repository of data related to the paper: "Automated IoT-Based Monitoring of Industrial Hemp in Greenhouses Using Open-Source Systems and Computer Vision".

The "Hemp_growth" folder contains hourly images of the monitored plants (C1, C2, C3, C4, and C5) from transplanting until 20 days after transplanting.

The "Hemp_water_stress" folder contains the train and test data and the trained model for water stress detection. The dataset was organised into four classes corresponding to different levels of water stress: healthy plants (no stress); plants with three days without irrigation; plants with six days without irrigation; and plants with nine days without irrigation. Training and test sets were prepared for each class with the following distributions (approx. 80% for training and 20% for test):

• 81 images of healthy plants for training and 20 for test;
• 90 images corresponding to 3 days of stress for training and 20 images for test;
• 70 images with 6 days of stress for training and 17 for test;
• 64 images of plants with 9 days of stress for training and 16 for test.