Crop Yield Prediction
Agriculture is the backbone of every economy and crop yield prediction is one of the most important issues in agriculture. The agricultural yield primarily depends on weather conditions (such as temperature, sunlight, etc.), pesticides, fertilizers, PH level, and other environmental conditions. Having accurate information on the history of crop yield is important for making decisions related to agricultural risk management and future predictions. The goal is to provide information to the grower knows how much yield they are about to expect.
We would use a variety of data from sources including climate data (such as humidity, sunlight, etc.), environmental data (such as temperature, soil conditions, etc.), and historical crop yield data in machine learning algorithms. Multiple machine learning models are developed, trained and evaluated to identify the model that achieves the closet accuracy in predicting crop yield.
Agriculture plays a critical role in the global economy. Predicting crop yield rates provides very important information for decision makers in the agriculture industry. It becomes necessary to increase the crop variety to produce disease-resistance offspring of the crops. It also helps in providing better and superior varieties based on the quality and quantity of the yield.
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