利用咖啡植物高度和光谱响应在遥控飞机多光谱图像中进行土壤压实制图

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Nicole Lopes Bento, Gabriel Araújo e Silva Ferraz, Lucas Santos Santana, Rafael de Oliveira Faria, Jhones da Silva Amorim, Mirian de Lourdes Oliveira e Silva, Michel Martins Araújo Silva, Diego José Carvalho Alonso
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引用次数: 0

摘要

土壤压实被认为是结构性土壤退化的主要威胁之一,它促进土壤颗粒密度增加,损害生态系统服务,损害植物发育,从而影响农业盈利能力。基于植被指数(VIs)和冠层高度模型(CHM)的航拍影像参数与咖啡种植区土壤压实度之间的关系,分析了远程驾驶飞机系统(RPAS)应用于咖啡种植区土壤压实的可行性。这项研究是在一个种植了14年的商业咖啡种植园进行的,种植的品种是Mundo Novo。进行了两次空中调查,第一次是确定CHM并确定采样点,第二次是进行VIs的辐射计算。在采样点收集了植物高度、土壤特征、土壤渗透阻力和生产力的数据。图像处理软件为Pix4D,数据分析软件为QGIS和RStudio。结果表明,不同株高区土壤化学分析差异无统计学意义;穿透阻力在株高带间存在显著的统计学差异,这与生产力数据相关;辐射数据与穿透阻力数据具有相关性,从而可以通过与压实数据的相关性来确定VIs (NDRE和MTCI),从而对该变量进行估计。通过这种方式,证明了使用RPAS来划定密实区监测咖啡作物高度变化的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Soil compaction mapping by plant height and spectral responses of coffee in multispectral images obtained by remotely piloted aircraft system

Soil compaction mapping by plant height and spectral responses of coffee in multispectral images obtained by remotely piloted aircraft system

Soil compaction is considered one of the main threats to structural soil degradation, and it promotes increased densification of soil particles, impairs ecosystem services, the plant development, and therefore affects agricultural profitability. In this sense, this study aimed to analyze the feasibility of using a Remotely Piloted Aircraft System (RPAS) by relating parameters derived from aerial images based on Vegetation Indices (VIs) and the Canopy Height Model (CHM) with soil compaction in a coffee plantation area. The study was conducted in a commercial coffee plantation with the cultivar Mundo Novo with 14 years of implantation. Two aerial surveys were carried out, the first to determine the CHM and define the sampling points and the second for radiometric calculations of VIs. In the sampling point were collected data plant height, soil characterization, soil penetration resistance and productivity. Images were processed by Pix4D software, and the data analysis at QGIS and RStudio. As at results, no statistically significant differences were detected between the different plant height zones in the soil chemical analysis; significant statistical differences between plant height zones were detected for penetration resistance, which is correlated to productivity data; and the radiometric data presented a correlation with the penetration resistance data, making it possible to determine VIs (NDRE and MTCI) with correlation to the compaction data allowing the estimation of such variable. In this way, the possibility of monitoring the height variations of the coffee crop using RPAS to demarcate compacted zones was evidenced.

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来源期刊
Precision Agriculture
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.
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