A CNN - Bidirectional LSTM Approach for Price Forecasting of Agriculture commodities in Gujarat

Modern approaches for predicting and investigating information from verifiable large-scale multi-dimensional data, with promising results and enormous potential. Technological advancements created an opportunity to forecast the future based on the historical data. Deep learning enables us to work with multi-dimensional large-scale data over a wide range of applications. and that Show evidence of improved result possibilities when there is huge data with recent trends as an input parameter. Because of failures and losses, the act of cultivating entails several obstacles. When there is a lot of trust, the forecasting of results has a lot of variables to consider. Farmers are unaware of the most recent improvements, leading to waste of their hard-earned money. This, too, leads to industrialization. The risk can be minimized to some extent if different steps are made to make farmers more aware of yield costs sooner rather than later. Forecasting assists enterprises, marginal farmers, and low-cost Agribusiness to achieve acceptable and achievable goals based on historical data. Firms can determine what level of change, development, or progress will be judged successful by using trustworthy information and data. The objectives of this article are to explore the impacts of agricultural forecasting. By addressing the identified obstacles, future researchers would be able to strengthen the prediction models in Agriculture domain. This paper's objective is to present the findings of a systematic market survey conducted to learn about farmers' crop selection strategies and employee productivity and efficiency, as well as their perspectives on agricultural mission planning, management of water resources, adaptation to climate change, and food waste control. Interoperability, scalability, accessibility, and usability are all considered during the examination. Future challenges for agricultural crops that can be cultivated and harvested for profit or subsistence are identified and summarized based on the evaluation results, identifying recent developments and presenting alternate future directions to marginal farmers and low-cost retailers who are impacted by high prices.