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Analyzing Crop Yield Using Machine Learning

Every farmer wants to know what kind of yields might be expected in the near future. This type of agricultural yield prediction relies heavily on machine learning. This also aids in the selection of which crops to cultivate and at what time of year. It is important to know the Yield in order to control our costs and plan our output accordingly, which will benefit both the farmer and the economy of the country as a whole. Since climatic circumstances have changed, farmers' previous experience on a specific field and crop is no longer as useful as it once was. Crop output can be predicted using past data, as well as weather conditions, as part of this project’s goal. We develop a model that can be used to predict agricultural yield in the future. In this case, it is preferable to use methods of machine learning. A set of data that serves as an input contains previously acquired data that was saved in a certain location. In order to predict crop production for the following year, we used machine learning to develop a simple prediction of harvest yield. The data was grouped using the Artificial neural network Algorithm which comes under deep learning during the training phase. During the testing phase, the yield number is approximated based on certain restrictions. When it comes to predicting harvest production, this new model outperforms the previous one by using neural networks as an additional tool.