Thermal Imaging and Disease Detection in Patharchatta Plant

Abstract

Plant diseases cause significant agricultural losses, affecting both crop yield and quality. Early detection is crucial for effective disease management. This study explores thermal imaging as a non-invasive method for identifying plant stress in Patharchatta (Kalanchoe pinnata). Two cases were analyzed: wilting due to dehydration and black spot disease from overwatering.Thermal thresholds of 16°C (early stress) and 18°C (critical damage) were experimentally identified, particularly in fungal-infected Patharchatta plants.A thermal image-based classification model was developed to support detection, achieving over 91% accuracy.The findings demonstrate that thermal imaging is a promising, real-time toolfor early disease detection, enabling proactive plant health management.

Introduction

Traditional plant disease detection methods are slow and often identify problems only after significant damage. This study explores thermal imaging as a fast, non-invasive way to detect early signs of disease by capturing temperature variations linked to plant stress. Thermal imaging can be used day or night, from a distance, and through obstacles like fog, making it efficient for real-time monitoring. The study focuses on Patharchatta (Kalanchoe pinnata), a medicinal succulent plant known for its healing properties but vulnerable to various diseases such as powdery mildew, root rot, and aphid infestation.

Patharchatta is rich in nutrients and bioactive compounds, offering anti-inflammatory, antioxidant, antimicrobial, pain-relieving, and other health benefits. Its diseases are mainly fungal, bacterial, viral, or insect-related, with symptoms affecting leaves, stems, and roots. Early detection through thermal imaging can prevent disease spread and preserve the plant’s medicinal quality.

Thermal imaging detects infrared radiation emitted by plants, translating heat patterns into visible images that reveal temperature changes caused by disease or stress. It is non-invasive, can detect water stress and infections, and can be deployed via handheld devices or drones for large-scale monitoring. This technology enables early, accurate disease detection, allowing farmers to respond swiftly, improve crop management, and reduce chemical use.

Conclusion

This research presents a novel, temperature-based thermal imaging model for early detection of two key diseases in Patharchatta plants. By introducing precise thermal thresholds (16°C and 18°C), and validating disease conditions through CNN predictions, it bridges a key gap in precision agriculture for medicinal crops.This study provides a foundational dataset and model for future development of AI-powered, real-time disease prediction tools in medicinal plant care. Key findings include: ?Early stress detection at 16°C, allowing intervention before visible symptoms appear. ? Temperatures exceeding 18°C indicate irreversible damage, particularly in fungal-infected plants. ? Overwatering accelerates disease progression, while dehydration causes a gradual decline in plant health. ? Thermal imaging can be integrated into precision agriculture, reducing crop losses and excessive pesticide use. These results highlight the potential of thermal imaging in sustainable farming, allowing farmers and researchers to detect diseases early and prevent large-scale agricultural damage.

References

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Copyright

Copyright © 2025 Swati Khatri, Abhishek Purohit. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.