探索菜作物的营养缺乏:利用先进的多维图像分析进行精确诊断
Jilong Xie1, Shanshan Lv1, Xihai Zhang1
1College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
本研究介绍了一种自动化系统,用于通过先进的图像分析和现场可编程门阵列 (FPGA) 来检测生菜的营养缺乏. 该系统准确地识别缺陷,改善作物管理和产量.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 图像处理 图像处理
背景情况:
- 生长,产量和质量受到营养缺乏的重大影响.
- 传统的营养检测方法耗时,劳动密集,缺乏自动化.
- 对作物营养状况的准确和快速诊断对于有效的农业管理至关重要.
研究的目的:
- 开发一种自动化系统,用于检测生菜中的营养缺乏.
- 提高作物营养物质诊断的准确性和效率.
- 为快速识别营养受损的生菜提供可靠的方法.
主要方法:
- 开发一种使用多维图像分析和现场可编程门阵列 (FPGA) 的生菜营养缺乏检测系统.
- 图像预处理技术的应用,包括动态窗口直方图中位过和自适应对比增强.
- 实现先进的图像细分算法 (值细分,坎尼边缘检测,梯度引导的自适应值细分) 用于组织差异化.
- 基于图像中识别的受影响组织的比例,对营养素缺乏的定量评估.
主要成果:
- 开发的系统在检测不同生长阶段的营养缺乏方面表现出很高的表现.
- 实现了0.944的平均精度,0.943的回忆率和0.943.943的F1得分.
- 与传统方法相比,显著提高了自动化,准确性和检测效率.
- 尽量减少样品干扰,确保可靠的诊断结果.
结论:
- 开发的系统为快速诊断生菜中的营养缺乏提供了可靠和有效的解决方案.
- 多维图像分析和FPGA技术的整合提高了作物营养评估的精度和自动化.
- 这项技术有可能优化农业实践并改善作物生产结果.
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