Image Processing Algorithm Design of Agricultural Internet of Things Platform Based on Big Data Analysis

Abstract

To solve the problems of low signal to noise ratio (SNR), poor processing effect, and long processing times for traditional image processing algorithms, an image processing algorithm for the agricultural Internet of Things platform based on big data analysis was designed. The big data analysis method was used for agricultural low-quality image recognition, and the Internet of Things platform uses sparse representation and a complete dictionary of low-quality image denoising processing with an improved histogram equalization method to enhance the image denoising result and get a high-quality image. Through this, the image processing algorithm design of the agricultural Internet of Things platform was completed. Experimental results show that the algorithm has high SNR and a good image processing effect, and the image processing time is always below 0.7 s, which meets research expectations.

Enhancing agricultural product trade efficiency through machine learning predictions and multi-objective optimization of financial strategies

“…Challenges are encountered in the existing methodologies when addressing the complexity inherent in agricultural financial data (Wu, 2022). Traditional statistical models, on the one hand, grapple with the intricacies of nonlinear, high-dimensional data, falling short in delineating complex interrelations within such data (Sun, 2022(Sun, , 2024. On the other hand, albeit early machine learning models have marked achievements in pattern recognition, they manifest limitations in managing time-series data and addressing long-term dependency concerns (Chen, 2022;Li et al, 2024;Sarkar et al, 2022;Seddik et al, 2023).…”