Adaboost CNN with Horse Herd Optimization Algorithm to Forecast the Rice Crop Yield
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Abstract
Over three billion people use rice every day, and it occupies about 12% of the nation's arable land. Since, due to the growing population and the latest climate change projections, it is critical for governments and planners to obtain timely and accurate rice yield estimates. The proposed work develops a rice crop yield forecasting model based on soil nutrients. Soil nutrients and crop production statistics are taken as an input for the proposed method. In ensemble learning, there are three categories, they are Boosting, Bagging and Stacking. In the proposed method, Boosting technique called Adaboost with Convolutional Neural Network is used to achieve the High accuracy by converting weak classifiers to strong classifiers. Adaptive data cleaning and imputation using frequent values are used as pre-processing approaches in the projected technique. A novel technique known as Convolutional neural network with adaptive boosting (Adaboost) technique is projected and can precisely handle more imbalanced datasets. The data weights are initialized; also the initial CNN is trained utilizing original weights of data. The weights of the second CNN are then modified utilizing the first CNN. These actions will be performed sequentially for all weak classifiers. An optimization algorithm called Horse Herd (HOA) is passed down in the proposed technique to find the optimal weights of the links in the classifier. The proposed method attains 95% accuracy, 87% precision, 85% recall, 5% error, 96% specificity, 87% F1-Score, 97% NPV and 12% FNR value.Thus the designed model as predicted the crop yield prediction in the effective manner.
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