Tomato Price Prediction Based on Regression

The digital transformation in our contemporary world is profoundly impacting every sector, particularly under the pervasive influence of the Information Technology (IT) field [1]. In the context of de-developing countries like India, the agricultural sector stands in need of substantial support for its advancement. Price prediction serves as a pivotal tool for both farmers and governmental bodies to make informed and effective decisions [2]. This study delves into the intricacies of predicting vegetable prices, harnessing the unique attributes of neural networks�such as self-adaptability, self-learning, and high fault tolerance [3]. Using the tomato as a case study, the model's parameters are meticulously examined through experimental analysis [4]. The conclusive findings of the Backpropagation neural network model unveil the absolute error percentages in monthly and weekly vegetable price predictions, offering a comprehensive analysis of the prediction accuracy [5].