Automatic Detection of Diseased Tomato Plants Using Thermal and Stereo Visible Light Images

TLDR

Accurate detection of plant diseases can reduce global agricultural losses, and while thermal imaging offers fast, non‑destructive scanning, its effectiveness is limited by environmental factors such as leaf angles and canopy depth. The study develops a machine‑learning system that fuses thermal, visible‑light, and depth data to remotely detect tomato plants infected with the powdery mildew fungus *Oidium neolycopersici*. The system extracts novel local and global statistical features from the multimodal images and integrates depth information to enhance detection accuracy. The feature set successfully identified plants that later developed disease naturally, demonstrating its ability to detect infections beyond initial inoculation.

Abstract

Accurate and timely detection of plant diseases can help mitigate the worldwide losses experienced by the horticulture and agriculture industries each year. Thermal imaging provides a fast and non-destructive way of scanning plants for diseased regions and has been used by various researchers to study the effect of disease on the thermal profile of a plant. However, thermal image of a plant affected by disease has been known to be affected by environmental conditions which include leaf angles and depth of the canopy areas accessible to the thermal imaging camera. In this paper, we combine thermal and visible light image data with depth information and develop a machine learning system to remotely detect plants infected with the tomato powdery mildew fungus Oidium neolycopersici. We extract a novel feature set from the image data using local and global statistics and show that by combining these with the depth information, we can considerably improve the accuracy of detection of the diseased plants. In addition, we show that our novel feature set is capable of identifying plants which were not originally inoculated with the fungus at the start of the experiment but which subsequently developed disease through natural transmission.