Highly efficient wheat lodging extraction algorithm based on two-peak search algorithm

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Precision Agriculture Pub Date : 2025-01-29 DOI:10.1007/s11119-025-10223-7

Xiuyu Liu, Jinshui Zhang, Xuehua Li, Kejian Shen, Shuang Zhu, Zhihua Liang

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引用次数: 0

Abstract

Purpose

Extracting the extent of wheat lodging is essential for post-disaster emergency response, disaster assessment, and accurate agricultural insurance claims. However, traditional methods for identifying lodged crops often lack flexibility, exhibit low levels of automation, and suffer from inefficiency.

Methods

This study proposes a rapid identification algorithm for wheat lodging, utilizing adaptive thresholding and a two-peak search of UAV imagery for reliable extraction of lodging regions. Initially, the red, green, and blue (RGB) visible band characteristics of UAV images after wheat lodging are analyzed. Subsequently, an Enhanced Wheat Lodging Index (EWLI) is proposed to quantitatively represent the lodging state. Second, a two-peak search dynamic thresholding algorithm, based on the square chunking of wheat lodging, is proposed to automatically determine thresholds for extracting winter wheat lodging regions.

Results

Experimental results demonstrate that the Enhanced Wheat Lodging Index (EWLI) effectively represents wheat lodging, while the two-peak search dynamic thresholding algorithm achieves robust performance. The proposed method achieves an overall accuracy of 96%, an F1 score of 0.97, and a Kappa coefficient exceeding 0.95, surpassing the performance of the OTSU method (maximum inter-class variance) and the KSW method (maximum entropy) with global thresholding.

Conclusion

The proposed method is applicable to diverse wheat lodging scenarios and demonstrates robust stability in identification accuracy. Key advantages include lightweight modeling, adaptive threshold determination, and the elimination of human intervention, making it an efficient, reliable, and highly practical approach for wheat lodging monitoring.

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目的提取小麦的虫害程度对于灾后应急响应、灾害评估和准确的农业保险理赔至关重要。本研究提出了一种快速识别小麦虫害的算法，利用自适应阈值和无人机图像的双峰搜索来可靠地提取虫害区域。首先，分析了小麦出苗后无人机图像的红、绿、蓝（RGB）可见光波段特征。随后，提出了一种增强的小麦出苗指数（EWLI）来定量表示出苗状态。结果实验结果表明，增强的小麦虫害指数（EWLI）能有效地表示小麦虫害，而双峰搜索动态阈值算法性能稳定。提出的方法总体准确率达到 96%，F1 得分为 0.97，Kappa 系数超过 0.95，超过了全局阈值的 OTSU 方法（最大类间方差）和 KSW 方法（最大熵）。其主要优点包括轻量级建模、自适应阈值确定和无需人工干预，是一种高效、可靠和实用性强的小麦纹枯病监测方法。

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来源期刊

Precision Agriculture 农林科学-农业综合

CiteScore

12.30

自引率

8.10%

发文量

103

审稿时长

>24 weeks

期刊介绍： Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.