salmaashraf23/Multispectral-images-for-improving-Agriculture

This project focuses on agricultural research in Al-Sharkia Governorate, Egypt, a crucial area for the nation's agriculture despite its extremely dry conditions and minimal rainfall. Using hyperspectral imaging and various vegetation indices, the project aims to enhance crop management efficiency, detect nutritional and moisture levels # Multispectral-images-for-improving-Agriculture ## Project Overview This project focuses on agricultural research in Al-Sharkia Governorate, Egypt, a crucial area for the nation's agriculture despite its extremely dry conditions and minimal rainfall. Using hyperspectral imaging and various vegetation indices, the project aims to enhance crop management efficiency, detect nutritional and moisture levels, and identify diseases and pests affecting crops. ### Objectives - **Evaluate agricultural soil and crop performance** using indices like NDVI, NDWI, Moisture, EVI, NDSI, and GNDVI. - **Classify agricultural land** based on its adaptation to climate changes. - **Improve crop management efficiency** and crop quality through advanced imaging techniques. ### Methodology - **Data Collection**: Experiments conducted in Al-Sharkia (coordinates: 31.06915283, 32.1268920) during the 2022/2023 season. - **Indices Calculation**: Using Sentinel-2A and B spectral band characteristics, various indices were calculated. - **Hyperspectral Imaging**: Captured precise spectral information for each pixel, enabling detailed analysis. - **Classification**: Dynamic API from NASA Copernicus system used for feature extraction, leading to classification into high agriculture, low agriculture, and not preferred for agriculture areas. ### Key Formulas - **NDVI = \((NIR - Red) / (NIR + Red)\)** - **NDWI = \((Green - NIR) / (Green + NIR)\)** - **Moisture = \((narrow - SWIR) / (narrow + SWIR)\)** - **EVI = \(G \times (NIR - Red) / (NIR + C1 \times Red - C2 \times Blue + L)\)** - **NDSI = \((Green - SWIR) / (Green + SWIR)\)** - **GNDVI = \((NIR - Green) / (NIR + Green)\)** ### Project Outcomes - Improved understanding of soil and crop dynamics in Al-Sharkia. - Enhanced methods for managing and predicting crop health and productivity. - Classification of agricultural land to optimize farming practices. This project leverages advanced imaging and machine learning techniques to provide actionabl …