Mapping grape production parameters with low-cost vehicle tracking devices

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Precision Agriculture Pub Date : 2024-03-02 DOI:10.1007/s11119-024-10125-0

{"title":"Mapping grape production parameters with low-cost vehicle tracking devices","authors":"","doi":"10.1007/s11119-024-10125-0","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>This study presents a method based on retrofitted low-cost and easy to implement tracking devices, used to monitor the whole harvesting process in viticulture, to map yield and harvest quality parameters in viticulture. The method consists of recording the geolocation of all the machines (harvest trailers and grape harvester) during the harvest to spatially re-allocate production parameters measured at the winery. The method was tested on a vineyard of 30 ha during the whole 2022 harvest season. It has identified harvest sectors (HS) associated with measured production parameters (grape mass and harvest quality parameters: sugar content, total acidity, pH, yeast assimilable nitrogen, organic nitrogen) and calculated production parameters (potential alcohol of grapes, yield, yield per plant) over the entire vineyard. The grape mass was measured at the vineyard cellar or at the wine-growing cooperative by calibrated scales. The harvest quality parameters were measured on grape must samples in a commercial laboratory specialized in oenological analysis and using standardized protocols. Results validate the possibility of making production parameters maps automatically solely from the time and location records of the vehicles. They also highlight the limitations in terms of spatial resolution (the mean area of the HS is 0.3 ha) of the resulting maps which depends on the actual yield and size of harvest trailers. Yield per plant and yeast assimilable nitrogen maps have been used, in collaboration with the vineyard manager, to analyze and reconsider the fertilization process at the vineyard scale, showing the relevance of the information.</p>","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"20 1","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Precision Agriculture","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s11119-024-10125-0","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

This study presents a method based on retrofitted low-cost and easy to implement tracking devices, used to monitor the whole harvesting process in viticulture, to map yield and harvest quality parameters in viticulture. The method consists of recording the geolocation of all the machines (harvest trailers and grape harvester) during the harvest to spatially re-allocate production parameters measured at the winery. The method was tested on a vineyard of 30 ha during the whole 2022 harvest season. It has identified harvest sectors (HS) associated with measured production parameters (grape mass and harvest quality parameters: sugar content, total acidity, pH, yeast assimilable nitrogen, organic nitrogen) and calculated production parameters (potential alcohol of grapes, yield, yield per plant) over the entire vineyard. The grape mass was measured at the vineyard cellar or at the wine-growing cooperative by calibrated scales. The harvest quality parameters were measured on grape must samples in a commercial laboratory specialized in oenological analysis and using standardized protocols. Results validate the possibility of making production parameters maps automatically solely from the time and location records of the vehicles. They also highlight the limitations in terms of spatial resolution (the mean area of the HS is 0.3 ha) of the resulting maps which depends on the actual yield and size of harvest trailers. Yield per plant and yeast assimilable nitrogen maps have been used, in collaboration with the vineyard manager, to analyze and reconsider the fertilization process at the vineyard scale, showing the relevance of the information.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

利用低成本车辆跟踪装置绘制葡萄生产参数图

摘要本研究介绍了一种基于加装的低成本且易于实施的跟踪设备的方法，该设备用于监测葡萄栽培的整个采收过程，以绘制葡萄栽培的产量和采收质量参数图。该方法包括在采收期间记录所有机器（采收拖车和葡萄采收机）的地理位置，以重新分配在酿酒厂测量的生产参数。该方法在 2022 年整个采收季期间对 30 公顷的葡萄园进行了测试。它确定了与整个葡萄园的测量生产参数（葡萄质量和采收质量参数：含糖量、总酸度、pH 值、酵母同化氮、有机氮）和计算生产参数（葡萄的潜在酒精含量、产量、单株产量）相关的采收区域（HS）。葡萄质量在葡萄园酒窖或葡萄种植合作社通过校准秤进行测量。采收质量参数是在一家专门从事酿酒分析的商业实验室，采用标准化方案对葡萄汁样本进行测量的。结果验证了仅凭车辆的时间和位置记录自动绘制生产参数图的可能性。这些结果还强调了所绘制地图在空间分辨率方面的局限性（HS 的平均面积为 0.3 公顷），这取决于实际产量和收获拖车的大小。与葡萄园管理者合作，利用单株产量和酵母同化氮地图分析和重新考虑葡萄园的施肥过程，显示了信息的相关性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

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.