Crop Type Classification Using Satellite Images

Novel progress in hyperspectral imaging and machine learning are revolutionizing the assessment of crop species, for which identification is central to feeding the world population. This research uses images obtained from the Deutsches Zentrum Earth Sensing Imaging Spectrometer (DESIS) to identify key arable crops in southeastern Hungary including hybrid corn, soybean, sunflower, and winter wheat. In this work, WA-CNN, Random Forest, and SVM machine learning algorithms are used to show how these models can improve the remote sensing of agricultural areas with precision thereby optimizing agricultural output and land usage. A notable aspect of this methodology is the application of cross-validation (CV), which plays a dual role: it not only compares the performance of different models but also optimizes weights of the ensemble, which are given to classification to enhance the reliability of the same. The results show that employing DESIS data allows for accurate determinations of crop development and yield prediction valuable information for farmers and authorities. Furthermore, since landscapes may be fragmented and fields differ in size, this study combines high spatial resolution imagery with high temporal frequency data. The study raises the argument that effective crop surveillance systems should be developed due to the beneficial effects they could have on agriculture as long as they increase the quality of decision-making in farming processes which will be crucial in meeting the ever-growing needs of the global population.