Abstract
There are many crop yield estimation techniques which are used in countries around the world, but the most effective is the one based on remote sensing data and technologies. However, remote sensing data which are needed to estimate crop yield is incomplete most of the time due to many obstacles such as climate conditions (percentage of cloud cover), and low temporal resolution. These problems reduce the effectiveness of the known crop yield estimation techniques and render them obsolete. There was many attempts to solve these problems by using high temporal resolution and low spatial resolution images. However, this type of images are suitable for very large homogeneous crop fields. To compensate for the lack of high spatial resolution satellite images, a new mathematical model is created. Based on the new mathematical model an intelligent system is implemented that includes the use of energy balance equation to improve the crop yield estimation. To verify the results of the intelligent system, several farmers are interviewed and information about their crops yield is collected. The comparison between the estimated crop yield and the actual production in different fields proves the high accuracy of the intelligent system.
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