Why are crop yield predictions important in agriculture and which algorithm is used in crop yield prediction using machine learning?
Advance crop yield prediction is important for formulation of various agriculture related policies related to foodgrain availability, crop insurance, raw material supply to industries, price and market related decisions, etc. The models based on Machine learning algorith viz, ANN,SVR, Random Forest, XGBoost, Decision Tree using weather, soil & crop penological parameters and remotely sensed vegetation indices as input may give a close to accurate level of precision for crop yield prediction.
Artificial neural networks are a more complex type of machine learning algorithm that is modelled after the structure and function of the human brain. They are particularly well suited for crop yield prediction because they can handle large amounts of data and identify complex patterns and relationships. Crop yield prediction is an essential predictive analytics technique in the agriculture industry. It is an agricultural practice that can help farmers and farming businesses predict crop yield in a particular season when to plant a crop, and when to harvest for better crop yield. An accurate crop yield prediction model can help farmers to decide on what to grow and when to grow. There are different approaches to crop yield prediction.One of the most important problems of agriculture is crop yield prediction. Agriculture yield depends on the various factors such as weather situation information about pesticides. In this proposed methodology, I have proposed random forest algorithm to predict better crop production. In this model, we have applied data set of crop yield from Kaggle websites, and it will be observed that 80% accuracy along with less classification error 20% trained on rapid miner tool of above models. These two algorithms, Support Vector Regression (SVR) and Linear Regression (LR), are quite suitable for validating the variable parameters in the predicting the continuous variable estimation with 140 data points that were acquired. The parameters mentioned above are key factors affecting the yield of crops. Artificial neural networks are a more complex type of machine learning algorithm that is modelled after the structure and function of the human brain. They are particularly well suited for crop yield prediction because they can handle large amounts of data and identify complex patterns and relationships.SVM algorithm is used for classification to classify the different parameters of the soil and predict the most suitable crop. Machine learning can help farmers optimize irrigation schedules and identify alternative water sources. Machine learning can help farmers adapt to changing conditions by identifying optimal growing conditions and developing early warning systems for extreme weather events. A crop recommendation system based on Support Vector Machines (SVM) is a machine learning algorithm that can predict which crops are best suited to a particular area based on various factors such as soil type, climate, and water availability.
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