Saudi Arabia is the home land of the date palm tree and the dates are considered to be one of the most important national products. As the dates are part of their heritage, therefore, Saudi Arabia is the largest consumer of dates. Saudi Arabia has more than fifty date-processing facilities, which process large amounts of these products. Right now, Saudi Arabia ranks second in the production of dates. There are more than twenty-five million date palm trees that cover more than 150,000 hectares of land in Saudi Arabia. Date production is estimated to be more than 1.1 million tons each year which accounts for around seventy two percent of the total agricultural output of Saudi Arabia. It is very vital to predict the yield so that stakeholders will be prepared to market their product in a better way. Crop yield estimation can be done either through conventional method or through image processing methods. The former are often costly, complex, time consuming methods, that cannot be applied on a large scale. So, it is essential to employ those methods for crop yield estimation that are time saving as well as cost effective and image processing method fulfils these conditions. Since image processing extracts different features from an image that can be used not only in recognizing different types of crops but also estimating their yield. Recently, crop yield estimation have been developed using Artificial Neural Networks (ANN) have exhibited improved performance and self-adaptability as compared to traditional statistical methods. Keeping in view the importance of this topic, this paper presents a framework for crop yield estimation through image processing by using the ANN.
Keywords
Date palm, Image Processing, Neural Networks, Deep Learning.
