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Agribusiness employs more than 66 percent of India’s rural population and is the country’s economic backbone. Beat crop growth is essential for practical farming since it increases soil diversity and actual design, and it may be grown in blended frameworks. Crop growth rates, applicability, and yields have not improved significantly over time in the United States. Crops are defined by their seasonality, derived nature of demand, and relatively inelastic pricing. The general purpose of this research is to demonstrate the usefulness of price forecasting for agricultural prices and validate it for rice, which is consumed more in Indian states, for the year 2022, using time series data from 2016 to 2021. Every year, data for 50 days is collected and multiplied. The range of ten and its multiple is used for predicting. The results were obtained through the use of univariate analysis. To develop grain price estimates, researchers used Autoregressive Integrated Moving Average (ARIMA) methods, and the precision of the forecasts was examined using conventional mean square error (MSE) and mean absolute percentage error (MAPE) standards. As proven by the outcomes of ARIMA price predictions, the ARIMA model’s efficacy as a tool for price forecasting was effectively demonstrated by realistic models of projected prices for 2020. Because the MSA and MAPE values were lower, the forecast was more accurate. In addition, the price forecasting in this model is dependent on government incentives.

The growth of the fish is influenced by a variety of scientific factors. So, profit can be easily achieved by using some clever techniques, for example, maintaining the correct pH level along with the dissolved oxygen (DO) level and temperature, as well as turbidity for good growth of fish. Fully grown fish are generally sold at a good price because price of fish in the market is governed by weight as well as size of nurtured fish. Artificial intelligence (AI)-based systems may be created to regulate key water quality factors including salinity, dissolved oxygen, pH, and temperature. The software programme operates on an application server and is connected to multiparameter water quality meters in this system. This study examines smart fish farming methods that show how complicated science and technology may be simplified for use in seafood production. This research focuses on the use of artificial intelligence in fish culture in this setting. The technical specifics of DL approaches used in smart fish farming which includes data and algorithms as well as performance was also examined. In a nutshell, our goal is to provide academics and practitioners with a better understanding of the current state of the art in DL implementation in aquaculture, which will help them deploy smart fish farming applications as well their benefits.

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