Stress detection in plants with thermal images using deep learning

The project presents a non-invasive technique used for stress detection in tomato plants using deep learning and thermal imaging. There have been may different methods used by the researchers for stress detection using thermal imaging and this shows an improved method by using a combination of both the numeric values of temperature obtained as well as the thermal images obtained by using thermal camera which is termed as multimodal analysis. The dataset of thermal images of tomato plants are collected by using thermal camera along with the corresponding temperature readings. The model is also deployed as an andrioid application where given an input image it gives output as stress or non-stress and then also provide the possibility for revival by further taking temperature values.

Papers based on the project:

1. Image Processing based application of Thermal Imaging for Monitoring Stress Detection in Tomato Plants- 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT).
2. Application of Deep Learning Coupled with Thermal Imaging in Detecting Water Stress in Plants-Design of Intelligent Applications Using Machine Learning and Deep Learning Techniques book by CRC press,2021.