A Review of Disease Detection in Leaves Using Image Processing Techniques Based on Thermal Camera
Keywords:
Thermal image, Electromagnetic spectrum, Healthy and diseased LeavesAbstract
Agriculture is the main source of vegetation to nourish the growing population of India. Plantation is the key source of energy and basic requirement for the prevention of global warming. The defects in the plant caused by several diseases becomes the vital problem for the economic, social and ecological development of the country. Hence, it is an important factor to diagnose the diseases that infect the plant at an earlier stage itself. Plant diseases pose significant threats to agricultural productivity and food security. Early detection and accurate diagnosis of these diseases are crucial for effective disease management. In the process of identification of diseases at an early stage quite number of imaging techniques are available. Some causes damage to the part of the plant considered for the detection of diseases. So, it is important to select the techniques that does not provide any harm to the plantation but at the same time act as an effective tool to identify the diseases with good accuracy. This review paper gives the brief evaluation of recent works carried out in early detection of diseases in plants using thermal imaging process and the analyzation of the imaging techniques by deep learning method. It also gives a detailed description of disease detection by different thermal imaging process and cataloguing technique with the assistance of machine learning mechanisms and image processing tools.
In recent years, image processing techniques based on thermal cameras have emerged as promising tools for non-invasive and efficient detection of plant diseases. This review aims to provide an overview of the application of image processing techniques, specifically those utilizing thermal cameras, for the detection of diseases in leaves. The review covers various aspects, including the principles of thermal imaging, data acquisition, image processing methods, and the challenges associated with disease detection. Furthermore, it discusses the potential of thermal imaging-based disease detection in precision agriculture and its future prospects
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