Phenotyping for the Real-time Detection of Plant Leaf Stress Using Infrared Thermal Imaging Sensors

Abstract

Plant stress in California has become a significant issue in recent years due to a combination of drought, malnutrition, and infections. There is an urgent need to develop a cost-effective, time-efficient, and reliable method to address this issue. This paper aims to develop a method of using infrared thermal imaging techniques to detect stress in plant leaves. The design of experiment (DOE) is divided into two phases. Phase one involved selecting three types of plants that represent significant stress factors in California - drought, infections, and malnutrition. The plants selected were raspberry, cherries, corn tomato, eggplant, and oleander. For each type of plant, three areas were chosen that each represented a stage of the plant’s stress: no stress (healthy), early stress, and fully stressed. Twenty points of surface thermal temperature were taken from each area of the plant leaf, and t-tests were conducted to calculate the p-value. The experiment indicates that thermal imaging techniques can be used for early detection in raspberry (drought and malnutrition) (p< 0.0001), cherries (drought) (p= 0.2996), corn (drought) (p< 0.0001), tomato (infections) (p< 0.0001), and eggplant (infections) (p< 0.0001), oleander (infections) (p< 0.0001). Phase two focused on developing a method to monitor the progressive development of plant stress throughout the entire drought process. A corresponding thermal model was built to understand the stress mechanism better for management of irrigation scheduling and plant phenotyping. The plants chosen were gardenia, tomato, and cucumber. In addition to recognizing thermal patterns throughout the process, an image processor was also created by code to calculate the percentage of healthy versus diseased area of the plant. The immediate application for this research is that it offers an advanced, non-invasive method for early detection of various plant stressors. This provides an effective solution for farmers to combat climate change and reduce plant losses due to infections, promoting much more sustainable agriculture.

Code and data availability

公開本文の所在を確認できませんでした。非公開または購読が必要な可能性があります。

No evidence-backed public reproduction asset is currently recorded.