A Novel Approach in Prediction of Crop Production Using Recurrent Cuckoo Search Optimization Neural Networks

Abstract

Data mining is an information exploration methodology with fascinating and understandable patterns and informative models for vast volumes of data. Agricultural productivity growth is the key to poverty alleviation. However, due to a lack of proper technical guidance in the agriculture field, crop yield differs over different years. Mining techniques were implemented in different applications, such as soil classification, rainfall prediction, and weather forecast, separately. It is proposed that an Artificial Intelligence system can combine the mined extracts of various factors such as soil, rainfall, and crop production to predict the market value to be developed. Smart analysis and a comprehensive prediction model in agriculture helps the farmer to yield the right crops at the right time. The main benefits of the proposed system are as follows: Yielding the right crop at the right time, balancing crop production, economy growth, and planning to reduce crop scarcity. Initially, the database is collected, and the input dataset is preprocessed. Feature selection is carried out followed by feature extraction techniques. The best features were then optimized using the recurrent cuckoo search optimization algorithm, then the optimized output can be given as an input for the process of classification. The classification process is conducted using the Discrete DBN-VGGNet classifier. The performance estimation is made to prove the effectiveness of the proposed scheme.

Enhancing Agriculture Prediction through AI and Parallel Distributed Computing: A Comprehensive Study on the Impact of Weather

“…It significantly affects crop output, disease incidence, and overall agricultural efficiency. This research intends to analyse the combined potential of AI and PDC in improving agricultural prediction capabilities [9,10].…”

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A Novel AI-Based Stock Market Prediction Using Machine Learning Algorithm

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Agriculture crop yield prediction using inertia based cat swarm optimization

“…In the DRL model, if the time series was much longer, then it caused the gradients to disappear or explode for predicting the crop yield. Rajagopal et al [21] developed a technique for predicting crop yield through recurrent cuckoo search optimization (RCSO) neural networks. The developed artificial intelligent (AI) system combined the several factors like rainfall, soil and crop yield production to forecast market price.…”