Neural Network Approach to Water-Stressed Crops Detection Using Multispectral WorldView-2 Satellite Imagery

The paper presents a method for automatic detection and monitoring of small waterlogged areas in farmland, using multispectral satellite images and neural network classifiers. In the waterlogged areas, excess water significantly damages or completely destroys the plants, thus reducing the average crop yield. Automatic detection of (waterlogged) crops damaged by rising underground water is an important tool for government agencies dealing with yield assessment and disaster control. The paper describes the application of two different neural network algorithms to the problem of identifying crops that have been affected by rising underground water levels in WorldView-2 satellite imagery. A satellite image of central European region (North Serbia), taken in May 2010, with spatial resolution of 0.5m and 8 spectral bands was used to train the classifiers and test their performance when it comes to identifying the water-stressed crops. WorldView-2 provides 4 new bands potentially useful in agricultural applications: coastal-blue, red-edge, yellow and near-infrared 2. The results presented show that a Multilayer Perceptron is able to identify the damaged crops with 99.4% accuracy. Surpassing previously published methods.