Survey for Agriculture Crop Yield Prediction and Recommendation Based on Deep Ensemble Learning Models

Since many people in India depend on agriculture for food and sustenance, it has long been the foundation of the country's economy. Agriculture development industrial resource that developed the Indian economy in recent years to achieve more crop production worldwide. The traditional methodologies carry the multi-feature constraints and forecasting rate to degrade the recommendation accuracy because of lover precision rate and accurate positive margins. Several approaches focus on different facets of feature engineering associated with Deep Learning (DL) and Machine Learning (ML) models. Recent developments in Artificial Intelligence (AI) utilizing multi-session datasets containing crop types and climate data have significantly enhanced seasonal crop suggestion prediction. Most of the development in Ensemble Learning Model (ELB) are projected in advanced developments to improve the crop recommendation based on seasonal crop recommendation with optimal feature selection and classification. An optimal learning algorithm, such as Particle Swarm Optimization (PSO), Support Vector Machine (SVM), Random Forest (RF), and Whale Optimization Algorithms (WHO), is important in the feature selection process. In similar the Recurrent Neural Network (RNN), Bi-LSTM, CNN, Multi-Layer Perceptron Neural Network (MLPNN) are importantly covered in this survey with its pros and cons. The main parameters considering to improve the parameters are precision rate, recall rate, f1 rate, with higher true positive rate. moreover, the negative impact of false rate degrades the prediction accuracy.