Publication

Abstract : Changes in crop prices are quite common everywhere irrespective of the location. These changes are often driven by various factors such as climatic conditions, market dynamics (demand and supply), government schemes, etc. Crop price prediction has several important purposes such as risk management and market planning. Crops are very essential for numerous reasons like food security, Trade, economic growth, ecosystem sustainability, etc. These crops encompass the health of the ecosystem. Accurate prediction of crop price plays a major role in supply chain market management, market stability, and global trade. To make an accurate crop price prediction, Deep-learning models were tested and trained with the data collected from the market. This data contains five different crops (onion, potato, apple, brinjal, orange), rainfall, temperature, wind, and humidity are the important parameters considered for this prediction. Multilayer Perceptron (MLPs), Convolution Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Artificial Neural Network (ANN), and LongShort Term Memory (LSTM) are different deep learning models that were tested and trained in this work.