Agricultural Fruit Prediction Using Deep Neural Networks

Abstract Agricultural production prediction is a challenging task in the deep neural network field. This paper presents a novel approach to fruit production prediction using deep neural networks to build a fast and reliable prediction system for agricultural production. In this article, we have considered different types of fruit production data (apples, bananas, citrus, pears, grapes, and total fruits), analyzed this data, and predicted the future production of these fruits using deep neural networks. The data are taken from the National Bureau of Statistics of Pakistan and the production output of major fruits. We implemented 3 different methods to predict the data for future fruit production. The first method is Levenberg-Marquardt optimization (LM), which was 65.6% accurate; the second method is called scale conjugate gradient back propagation (SCG), which had an accuracy of 70.2%, and the third method, is Bayesian regularization back propagation (BR), which was 76.3% accurate. These methods to predict fruit production are applicable in developing countries because they can compare production with increasing populations and assist in making new policies to increase production. The estimated results reveal that the government of Pakistan needs to further increase fruit production and create better policies for farmers to improve their production.