New intelligent particle swarm optimization algorithm with extreme learning machine for forecasting Pattavia pineapple productivity: case study of Loei and Nong Khai provinces in Thailand

Abstract

This research designs and develops a software innovation for Pattavia pineapple cultivation and productivity distribution planning to increase income for farmers. This research formulated and introduced an innovative machine learning (ML) model, the new model is called a new intelligent particle swarm optimization algorithm with extreme learning machine (NIPSO-ELM), to forecast Pattavia pineapple productivity with a notable degree of precision and dependability. In this work, the artificial neural network (ANN) and the standard ELM was built, and assessed for its ability to forecast the productivity of Pattavia pineapples. The findings indicate that the ELM neural network is an innovative model characterized by its straightforward architecture and exceptional performance. Moreover, the utilization of particle swarm optimization (PSO), ant colony optimization (ACO) and the NIPSO algorithms significantly enhanced ELM performance when forecasting the productivity of Pattavia pineapples. The NIPSO-ELM model emerged as the most optimal ML model for accurately, reliably and stably forecasting the productivity of Pattavia pineapples in practical scenarios. The most optimal NIPSO-ELM models for the Loei provinces, Thailand exhibit the following performance metrics: RMSE = 304.36389, MAE = 243.29531, MAPE = 0.03753 and MASE = 0.93157. The most optimal NIPSO-ELM models for the Nong Khai provinces, Thailand exhibit: RMSE = 304.57352, MAE = 244.67834, MAPE = 0.03756 and MASE = 0.93296, respectively.

Keywords:

• Pineapple
• extreme learning machine
• forecasting
• metaheuristic algorithms
• applied mathematics
• optimization algorithms
• agricultural production planning
• artificial neural networks

Review Editor:

• Wang Lin
• Full Professor
• Computer Science and Information Engineering
• No. 259
• Wen-Hwa 1st Road
• Kweishan
• Chang Gung University
• TAIWAN

Subjects:

• Mathematics & Statistics
• Applied Mathematics
• Computer Science
• Algorithms and Complexity
• Artificial Intelligence

Acknowledgments

This work comes under the Fundamental Fund 2022, and the research project is the design and development of agricultural innovative application software for pineapple plantation management granted by the National Science, Research and Innovation Fund. The author thanks the Faculty of Interdisciplinary Studies, Khon Kaen University, Nong Khai Campus and the Khon Kaen University, Khon Kaen Campus in Thailand, for supporting this work.

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

The Research by Khon Kaen University, Faculty of Interdisciplinary Studies has received funding support from the National Science, Research and Innovation Fund.