Precision mapping and treatment of spring dead spot in bermudagrass using unmanned aerial vehicles and global navigation satellite systems sprayer technology

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Precision Agriculture Pub Date : 2025-02-28 DOI:10.1007/s11119-025-10231-7

Caleb Henderson, David Haak, Hillary Mehl, Sanaz Shafian, David McCall

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Abstract

Spring dead spot is a disease of bermudagrass (Cynodon dactylon L. Pers) caused by Ophiosphaerella spp., of fungi which infect the below ground structures of plants, causing damage to the turf canopy. Previous research suggests that precision management strategies based on manually identified disease within unmanned aerial vehicle (UAV) imagery using GIS software and global navigation satellite systems (GNSS)-equipped sprayers can reduce the fungicide required for spring dead spot management. However, this methodology is time consuming and impractical for golf course superintendents. This paper introduces a novel approach to spring dead spot identification utilizing a custom Python script, the Simple Ophiosphaerella Damage Detector (SODD), to identify and record locations of spring dead spot from UAV imagery using basic feature extraction techniques. Initial tests comparing the outputs from SODD to spring dead spot manually identified by researchers on four fairways, comparisons of K-means cluster maps showed similarities ranging between 71 and 88% although incidence counts were inconsistent. Precision treatment methods based on SODD were evaluated across 16 golf course fairways at three locations in Virginia organized as a randomized complete-block design with four replications and four treatment methods; spot and zonal treatments based on SODD identified incidence and density, respectively, compared against full-coverage and non-treated controls. Applications were made with a Toro Multipro5800 with GeoLink GNSS-equipped sprayer in Fall of 2021. Spot and zonal treatment strategies showed similar control to full-coverage applications (p≤0.001) while reducing the percentage of the fairways treated by 48% and 52%, respectively (p≤0.001). These results highlight the capabilities for SODD as a tool for disease map generation.

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利用无人驾驶飞行器和全球导航卫星系统喷雾器技术对百慕大草春季枯斑进行精确测量和处理

春死斑病是一种由蛇皮藻属真菌引起的百草病害，这种真菌感染植物的地下结构，造成草皮冠层的破坏。先前的研究表明，使用GIS软件和配备全球导航卫星系统（GNSS）的喷雾器，基于无人机（UAV）图像中人工识别疾病的精确管理策略可以减少春季死斑管理所需的杀菌剂。然而，这种方法对高尔夫球场负责人来说是费时且不切实际的。本文介绍了一种新的弹簧死点识别方法，该方法利用自定义Python脚本-简单Ophiosphaerella Damage Detector (SODD)，使用基本特征提取技术从无人机图像中识别和记录弹簧死点的位置。最初的测试将SODD的输出与研究人员在四个球道上手动确定的弹簧死点进行了比较，K-means聚类图的比较显示相似性在71%到88%之间，尽管发生率不一致。基于SODD的精确治疗方法在弗吉尼亚州三个地点的16个高尔夫球场球道中进行了评估，组织为随机完全区设计，有4个重复和4种治疗方法；与全覆盖和未处理对照相比，基于SODD的定点和分区处理分别确定了发病率和密度。2021年秋季，Toro Multipro5800与配备GeoLink gnss的喷雾器进行了应用。现场和区域处理策略显示出与全覆盖应用相似的控制效果（p≤0.001），同时分别减少了48%和52%的球道处理百分比（p≤0.001）。这些结果突出了SODD作为疾病地图生成工具的能力。

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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.