Volume 20 No 12 (2022)
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Agriculture has been identified as one of the most important sectors for the growth of nations. Agriculture is affected by the impact on the economy of the nation and on food grain statistics around the world. The challenge always lies in achieving sustainable crop production for agriculturists. Changing environmental conditions have always made it difficult for farmers to achieve optimal crop yields. The unpredictability of crop yields is mostly caused by differences in land types, the availability of resources, and changes in the weather. Consequently, scientists around the world are exploring techniques that can be used to estimate crop yield efficiently and with excellent accuracy in the months ahead. This is so that farmers may prepare for these issues in the future and act appropriately. In this article, we cover a variety of approaches to agricultural production prediction that involve machine learning and deep learning algorithms. In order to elaborate on yield predictions, a collection of data over a long period of time is used to implement various algorithms. Additionally, because we are interested in estimating the yield in light of the classification methods predicting the yield with respect to classification techniques, Unpredictable weather conditions, poor harvesting, and irrigation techniques, and mismanagement of livestock are some of the reasons for reduced food production. we have conducted a comparative analysis of which classification algorithm is the most appropriate for this purpose.
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