Calculating Leaf Area Index Using Neural Network and WorldView 3 Multispectral Imagery

Leaf Area Index (LAI) holds significant importance as a specific characteristic of Leaf Areas in the field of smart agriculture. This study explores the estimation of LAI using a multi-spectral image from WorldView 3 satellite. The image combines 8 VNIR bands and has a spatial resolution of 1.24m. To overcome the limited amount of available data, the image was split into smaller subsets called paxels, resulting in 500 paxels for training and testing. For enhancing machine learning models. performance, the standardisation of a dataset is made, after that, a Multilayer Perceptron with a specific architecture aimed to predict LAI from the multiple bands is trained. The achieved results showed promising performance in LAI prediction. Overall, the study demonstrates the potential of using satellite imagery and machine learning algorithms to improve our understanding of crop health and productivity.