Background: In the agriculture development and growth, the intelligent machinery and equipment plays an important role. Various researchers are involved for implementing the research and patents to aid the smart agriculture and author reviewer that machine leaning technologies are providing the best support for this growth.
Method: To explore machine learning technology and machine learning algorithms, mostly based on swarm intelligence optimization, and their applications are studied. An optimized V3CFOA-RF model is built through V3CFOA. The algorithm is tested in the data set collected concerning rice pests, later analysed and compared in detail with other existing algorithms.
Results: The research result shows that the model and algorithm proposed are not only more accurate in recognition and prediction, but also solve the time lagging problem to a degree.
Conclusion: The model and algorithm helped realise a higher accuracy in crop pest prediction, which ensures a more stable and higher output of rice. Thus they can be employed as an important decision-making instrument in the agricultural production sector.
Swarm Intelligence Optimization, Machine Learning Algorithms, V3CFOA, V3CFOA-RF Model
Anhui Vocational college of Electronics & Information Technology ,Anhui Bengbu 233030