Site and time-specific early weed control is able to reduce herbicide use in maize - a case study

IF 2.6 3区 农林科学 Q1 AGRONOMY
N. Nikolić, Davide Rizzo, E. Marraccini, Alicia Ayerdi Gotor, Pietro Mattivi, P. Saulet, A. Persichetti, R. Masin
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引用次数: 10

Abstract

Remote sensing using unmanned aerial vehicles (UAVs) for weed detection is a valuable asset in agriculture and is vastly used for site-specific weed control. Alongside site-specific methods, timespecific weed control is another critical aspect of precision weed control where, by using different models, it is possible to determine the time of weed species emergence. In this study, site-specific and time-specific weed control methods were combined to explore their collective benefits for precision weed control. Using the AlertInf model, which is a weed emergence prediction model, the cumulative emergence of Sorghum halepense was calculated, following the selection of the best date for UAV survey when the emergence was predicted to be at 96%. The survey was executed using a UAV with visible range sensors, resulting in an orthophoto with a resolution of 3 cm, allowing for good weed detection. The orthophoto was post-processed using two separate methods: an artificial neural network (ANN) and the visible atmospherically resistant index (VARI) to discriminate between the weeds, the crop and the soil. Finally, a model was applied for the creation of prescription maps with different cell sizes (0.25 m2, 2 m2, and 3 m2) and with three different decision-making thresholds based on pixels identified as weeds (>1%, >5%, and >10%). Additionally, the potential savings in herbicide use were assessed using two herbicides (Equip and Titus Mais Extra) as examples. The results show that both classification methods have a high overall accuracy of 98.6% for ANN and 98.1% for VARI, with the ANN having much better results concerning user/producer accuracy and Cohen's Kappa value (k=83.7 ANN and k=72 VARI). The reduction percentage of the area to be sprayed ranged from 65.29% to 93.35% using VARI and from 42.43% to 87.82% using ANN. The potential reduction in herbicide use was found to be dependent on the area. For the Equip Ac ce pt ed p ap er herbicide, this reduction ranged from 1.32 L/ha to 0.28 L/ha for the ANN; with VARI the reduction in the amounts used ranged from 0.80 L/ha to 0.15 L/ha. Meanwhile, for Titus Mais Extra herbicide, the reduction ranged from 46.06 g/ha to 8.19 g/ha in amounts used with the ANN; with VARI the reduction in amounts used ranged from 27.77 g/ha to 5.32 g/ha. These preliminary results indicate that combining site-specific and time-specific weed control, has the potential to obtain a significant reduction in herbicide use with direct benefits for the environment and on-farm variable costs. Further field studies are needed for the validation of these results.
特定地点和时间的早期杂草控制能够减少玉米除草剂的使用——一个案例研究
利用无人机(uav)进行杂草检测的遥感技术在农业中是一项宝贵的资产,并广泛用于特定地点的杂草控制。除了特定地点的方法外,特定时间的杂草控制是精确杂草控制的另一个关键方面,通过使用不同的模型,可以确定杂草物种出现的时间。在本研究中,将特定地点和特定时间的杂草控制方法相结合,探讨它们对精确杂草控制的集体效益。利用杂草出苗率预测模型AlertInf模型,选择预测出苗率为96%的最佳无人机调查日期,计算了高粱的累计出苗率。该调查使用了带有可见距离传感器的无人机,产生了分辨率为3厘米的正射影像仪,可以很好地检测杂草。采用人工神经网络(ANN)和可见光大气抗性指数(VARI)两种不同的方法对正直射像进行后处理,以区分杂草、作物和土壤。最后,应用一个模型创建不同单元格大小(0.25 m2、2 m2和3 m2)的处方地图,并基于识别为杂草的像素(>1%、>5%和>10%)设置三种不同的决策阈值。此外,以两种除草剂(Equip和Titus Mais Extra)为例,评估了除草剂使用的潜在节省。结果表明,两种分类方法的ANN和VARI的总体准确率均达到了98.6%和98.1%,其中ANN在用户/生产者准确率和Cohen’s Kappa值(k=83.7 ANN和k=72 VARI)方面的结果要好得多。VARI和ANN对喷淋面积的减少率分别为65.29% ~ 93.35%和42.43% ~ 87.82%。发现减少除草剂使用的潜力取决于地区。对于装备Ac除草剂,这种减少幅度从1.32 L/ha到0.28 L/ha不等;使用VARI,用量的减少幅度从0.80升/公顷到0.15升/公顷不等。与此同时,对Titus Mais Extra除草剂,ANN用量减少46.06 g/ha ~ 8.19 g/ha;使用VARI,用量减少幅度从27.77克/公顷到5.32克/公顷不等。这些初步结果表明,结合特定地点和特定时间的杂草控制,有可能显著减少除草剂的使用,并对环境和农场可变成本产生直接效益。需要进一步的实地研究来验证这些结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.20
自引率
4.50%
发文量
25
审稿时长
10 weeks
期刊介绍: The Italian Journal of Agronomy (IJA) is the official journal of the Italian Society for Agronomy. It publishes quarterly original articles and reviews reporting experimental and theoretical contributions to agronomy and crop science, with main emphasis on original articles from Italy and countries having similar agricultural conditions. The journal deals with all aspects of Agricultural and Environmental Sciences, the interactions between cropping systems and sustainable development. Multidisciplinary articles that bridge agronomy with ecology, environmental and social sciences are also welcome.
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