3D multispectral imaging for early detection of disease in plant

Plant diseases can cause serious damage to crop affecting agricultural productivity and food security. Early detection is crucial to prevent their spread and minimise losses. However, traditional methods such as visual inspection are often time-consuming and may not be accurate, particularly in the early stages of infection. This study explores the use of 3D multispectral imaging as an advanced approach to detecting plant diseases by analysing both spectral and structural changes in plants. A UAV equipped with multispectral camera will be used to capture images of selected plant samples. The collected data will be processed using Pix4D and Meshlab to generate 3D model and calculate vegetation indices such as NDVI and NDRE. A ground truth survey will also conduct to compare the imaging results with actual plant conditions. The expected findings indicate that 3D multispectral imaging can detect early symptoms of plant diseases more effectively than traditional methods. The combination of spectral analysis and 3D modelling will provide a clearer understanding of plant health, enabling earlier and more accurate disease identification. Overall, this study can highlight the potential of 3D multispectral imaging as a valuable tool for improving plant disease detection. By integrating this technology into agriculture, farmers can take quicker action to manage plant health and reduce crop losses.