ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1639016
Light Adaptive Image Enhancement for Improving Visual Analysis in Intercropping Cultivation
Provisionally accepted
- 1Jiangsu University, Zhenjiang, China
- 2Taizhou University, Taizhou, China
- 3Chinese Academy of Agriculture Mechanization Sciences Group Co., Ltd.,, beijing, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Intercropping maize and soybean with distinct plant heights is a typical practice in diversified cropping systems, where shadows cast by taller maize plants onto soybean rows pose significant challenges for image based recognition. This study conducted experiments throughout the entire soybean-maize intercropping period to address illumination variation. Based on the height difference between crops, solar elevation angle, and light intensity at the top of the soybean canopy, an illumination compensation regression model was developed. The model was applied to correct soybean canopy images and compared against traditional enhancement methods, including histogram equalization, Multi-Scale Retinex (MSR), and gamma correction. Quantitative evaluation using peak signal-to-noise ratio (PSNR) showed the proposed method achieved 40.79 dB, indicating superior image quality. Furthermore, analysis of RGB and HLS channels revealed a consistent increase in brightness from left (darker) to right (brighter) across the images. Specifically, green channel values rose from 150-230 to 180-240, and overall RGB values exceeded 150, suggesting improved brightness and reduced local fluctuations. Brightness increased from 90-200 to 150-220, with the left region rising from 125 to 175. Finally, a comparison of channel-wise standard deviations among methods showed that the proposed algorithm exhibited lower variance in the green (G) and hue (H) channels, with favorable consistency across others. These results demonstrate the model's effectiveness in achieving smoother brightness transitions, thereby enhancing image uniformity and mitigating the negative impact of uneven illumination on recognition tasks.
Keywords: Illumination compensation, intercropping, Height difference, Solar elevation angle, Growth stage
Received: 01 Jun 2025; Accepted: 01 Aug 2025.
Copyright: © 2025 Zhong, Yang, Wang, Dong, Wang, Jia, Ou and Yan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xiaowen Wang, Jiangsu University, Zhenjiang, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.