轨道多光谱成像：鉴别咖啡线虫管理策略的工具

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Precision Agriculture Pub Date : 2024-09-04 DOI:10.1007/s11119-024-10188-z

Vinicius Silva Werneck Orlando, Bruno Sérgio Vieira, George Deroco Martins, Everaldo Antônio Lopes, Gleice Aparecida de Assis, Fernando Vasconcelos Pereira, Maria de Lourdes Bueno Trindade Galo, Leidiane da Silva Rodrigues

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引用次数: 0

摘要

背景基于多光谱成像的遥感技术可能有助于检测农业中的植被应激反应。目的评估轨道多光谱成像技术在鉴别减少植物寄生线虫数量的最有效策略方面的潜力，从而防止咖啡生产中的产量损失。方法用 11 种处理方法处理咖啡植株，包括芽孢杆菌属分离物、商业生物产品、商业化学杀线虫剂和水（对照组）。对土壤中的初始和最终线虫数量进行了量化，并使用 Planet 轨道多光谱传感器收集了表面反射率数据。结果施用淀粉芽孢杆菌分离物 B266 和枯草芽孢杆菌分离物 B33 后，植物寄生线虫的数量分别减少了 35.90% 和 55.13%。结论：轨道多光谱成像技术可以分辨出咖啡植物线虫管理中最有效的处理方法，凸显了其作为农业辅助工具的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Orbital multispectral imaging: a tool for discriminating management strategies for nematodes in coffee

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Orbital multispectral imaging: a tool for discriminating management strategies for nematodes in coffee

Background

Remote sensing based on multispectral imaging may be useful for detecting vegetation stress responses in agriculture.

Objectives

To evaluate the potential of orbital multispectral imaging in discriminating the most effective strategies for reducing plant-parasitic nematode populations, thereby preventing yield losses in coffee production.

Methods

Coffee plants were treated with eleven treatments, including Bacillus spp. isolates, commercial biological products, commercial chemical nematicides, and water (control group). Initial and final nematode populations in the soil were quantified, and surface reflectance data were collected using the Planet orbital multispectral sensor. The data were classified using the random tree algorithm.

Results

The population of plant-parasitic nematodes was reduced by 35.90% and 55.13% following the application of B. amyloliquefaciens isolate B266 and B. subtilis isolate B33, respectively. Under the conditions of this experiment, multispectral imaging accurately discriminated the most nematicidal treatments, with a global accuracy of 80%.

Conclusions

Orbital multispectral imaging can discriminate the most effective treatments used for nematode management in coffee plants, highlighting its potential as a supportive tool in agriculture.

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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.