IoT Based Root Stress Detection for Lettuce Culture Using Infrared Leaf Temperature Sensor and Light Intensity Sensor

摘要

Root stress is a big problem for lettuce farming in tropical climates, especially temperature root stress. Black root rot, a final stage of the temperature root stress, leads to huge production loss. This paper presents the IoTs based root stress detection system for lettuce cultures. The proposed detection algorithm is based on the leaf energy balance and transpiration patterns. Unlike image sensors based detection methods, leaf energy balance principle and transpiration patterns measured from a lettuce leaf are considered as the key features to address the lettuce root stress conditions. The challenge of detecting lettuce stress by using a leaf sensor is to estimate the non-linear function of stomatal conductance. This paper has clarified the concept of detecting lettuce root stress using the transpiration patterns as well. Graphically, the combination of infrared temperature and light intensity sensors is appropriate to deal with the lettuce root stress detection. The proposed detection algorithm has been designed to detect three conditions of root stress problems: normal, root stress, and black root rot conditions. The infrared sensors are very suitable for the sensitive leaf like lettuce. To evaluate the proposed leaf sensor, the experiments are set up to show that the proposed detection algorithm can accurately detect the temperature root stress in different conditions. Moreover, the detection algorithm based leaf area index (LAI) has been discussed to the proposed detection algorithm. In addition, the performance of the proposed detection algorithm has been compared to the LAI based algorithm. The detection accuracy of the proposed detection method is 95% with different root stress conditions.