Application of population intelligence optimization algorithms to environmental monitoring problems in maize fields.

Problems in intelligent farming include soil erosion, pests, diseases, etc. Population intelligence optimization algorithms have a wide range of application prospects in farming environments. While in practical applications, there are problems such as vulnerability to disturbance, tendency to fall into local optimization, and lack of accuracy. Thus, in this study, the Moth Flame Optimization (MFO), Grey Wolf Algorithm (GWO), and Integrated Particle Algorithm (PSO) were combined with improved algorithms for optimization. The Sine Cosine Strategy (SCS) was used to promote the MFO algorithm and was adopted for the land erosion prediction problem based on the Kernel-Based Extreme Learning Machine (KELM) algorithm. The multi-strategy mechanism was used to improve the GWO as a basis for designing an accurate fertilization model. The traditional PSO algorithm was improved by applying elite augmentation and applied to the segmentation of maize disease images. The results showed that the improvement of SMFO-KELM for KELM effectively improved the prediction ability of soil erosion classification. In intelligent agriculture, the performance of multi-strategy GWO was distinctly better than other improved algorithms. In contrast to traditional PSO algorithm, the structure similarity index of the elite enhanced PSO algorithm was improved from 0.88 to 0.95, and the feature similarity index was improved from 0.72 to 0.86 and could obtain better segmentation accuracy than other similar algorithms in solving the overall effect of multi-threshold segmentation for maize rust spot disease. The accuracy of the population intelligence algorithm was improved, and the problem of interference was solved. The use of the population intelligence optimization algorithm realized real-time monitoring and intelligent management of the corn field environment, including the monitoring and regulation of soil moisture, temperature, nutrient status, and other parameters to promote the growth and development of maize and improve the yield and quality of maize, which helped to promote the development of intelligent agriculture and realize the refinement and intelligent management of the agricultural production process. [ABSTRACT FROM AUTHOR]