A Neural Network-Based Index (nNDVI) for Estimating the Normalized Difference Vegetation Index (NDVI) from Standard RGB Images

Le résumé fourni par la source

An important global research topic is the analysis of the crop status, viability, and disease status of vegetables, fruits, and plants in agricultural areas. The Normalized Vegetation Difference Index (NDVI) is commonly used to analyze these conditions by using near-infrared (NIR) features in satellite images or multispectral cameras, such as Lansat-8, to produce NDVI maps. However, these methods have limitations such as high cost and difficulty in accessing images. To address these limitations, this study proposes a new neural network-based index called nNDVI, which uses a Multi-Layer Perceptron (MLP), an Artificial Neural Network (ANN), to convert the NDVI value from standard RGB images. The nNDVI allows for the analysis of vegetation in agricultural areas using low-cost RGB cameras. The MLP model was trained with R (red), G (green), and B (blue) values as input, and real NDVI values for the Swiss forest and Togo farm images were obtained with the MicaSenseAltum camera. The results of testing the model on the dataset showed an accuracy of 92.013% when comparing the nNDVI values obtained with the RGB cameras to the actual NDVI values. Thus, the proposed method demonstrates the ability to use nNDVI maps obtained using low-cost RGB cameras as an alternative to NDVI maps obtained using high-cost multispectral cameras. Overall, this study makes a valuable contribution to the field of agricultural research by presenting a cost-effective and accessible method for analyzing vegetation in agricultural areas.

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