Pub Date : 2017-07-01DOI: 10.1017/S2040470017000346
G. Vellidis, F. Morari, A. Battisti, A. Berti, M. Borin, J. Broder, M. Cabrera, Raffaella Cattarinussi, D. Franklin, V. Mcmaken, D. Shilling, W. Vencill
The University of Georgia (USA) is partnering with the University of Padova (Italy) for a dual Master’s degree program in sustainable agriculture, promoting collaboration on some of the biggest challenges facing agriculture today. This innovative program which was launched during 2016 provides students with outstanding training and a unique opportunity to learn about the challenges, opportunities, and leading edges of precision agriculture on another continent – an experience which will serve graduates well when they enter the job market in an increasingly global economy. This paper presents the goals of the program, the curriculum, and describes the opportunities available to prospective students. In addition it describes the process of developing the dual degree which can be used as guide by others wishing to develop similar programs.
{"title":"From a Precision Agriculture Consortium to a Dual Master’s Degree in Sustainable Agriculture","authors":"G. Vellidis, F. Morari, A. Battisti, A. Berti, M. Borin, J. Broder, M. Cabrera, Raffaella Cattarinussi, D. Franklin, V. Mcmaken, D. Shilling, W. Vencill","doi":"10.1017/S2040470017000346","DOIUrl":"https://doi.org/10.1017/S2040470017000346","url":null,"abstract":"The University of Georgia (USA) is partnering with the University of Padova (Italy) for a dual Master’s degree program in sustainable agriculture, promoting collaboration on some of the biggest challenges facing agriculture today. This innovative program which was launched during 2016 provides students with outstanding training and a unique opportunity to learn about the challenges, opportunities, and leading edges of precision agriculture on another continent – an experience which will serve graduates well when they enter the job market in an increasingly global economy. This paper presents the goals of the program, the curriculum, and describes the opportunities available to prospective students. In addition it describes the process of developing the dual degree which can be used as guide by others wishing to develop similar programs.","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"78 1","pages":"738-742"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83754750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-01DOI: 10.1017/S2040470017001182
L. Xia, R. R. Zhang, L. P. Chen, Y. Wen, F. Zhao, J. Hou
In this study, the biomass of winter wheat was estimated by using hyperspectral data obtained from a hyperspectral camera on an Unmanned Aerial Vehicle (UAV). Every two bands from the hyperspectral data were selected to calculate two kinds of vegetation indexes: the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI). Linear models were established between winter wheat biomass and those indexes, and coefficient of determination R² was used to draw the two-dimensional distribution of R² values. The comparison between NDVI and RVI for pixel covered by soil and wheat showed that RVI is more efficient to mask the influence from soil than NDVI. For calculating the NDVI, optimal bands are located mainly around 820 nm and 725 nm to 750 nm. For assessing RVI, the wavelength range from 820 to 832 nm, 794 to 808 nm, 770 to 788 nm, 725 nm to 750 nm and 890 nm for RVI are most suitable. Those optimal bands can achieve a coefficient of determination R² higher than 0.88 by using the linear regression model in the study.
{"title":"Retrieving wheat Biomass by using a hyper-spectral device on UAV","authors":"L. Xia, R. R. Zhang, L. P. Chen, Y. Wen, F. Zhao, J. Hou","doi":"10.1017/S2040470017001182","DOIUrl":"https://doi.org/10.1017/S2040470017001182","url":null,"abstract":"In this study, the biomass of winter wheat was estimated by using hyperspectral data obtained from a hyperspectral camera on an Unmanned Aerial Vehicle (UAV). Every two bands from the hyperspectral data were selected to calculate two kinds of vegetation indexes: the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI). Linear models were established between winter wheat biomass and those indexes, and coefficient of determination R² was used to draw the two-dimensional distribution of R² values. The comparison between NDVI and RVI for pixel covered by soil and wheat showed that RVI is more efficient to mask the influence from soil than NDVI. For calculating the NDVI, optimal bands are located mainly around 820 nm and 725 nm to 750 nm. For assessing RVI, the wavelength range from 820 to 832 nm, 794 to 808 nm, 770 to 788 nm, 725 nm to 750 nm and 890 nm for RVI are most suitable. Those optimal bands can achieve a coefficient of determination R² higher than 0.88 by using the linear regression model in the study.","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"104 1","pages":"833-836"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89734751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-01DOI: 10.1017/S2040470017001078
M. Azimi, Z. Shukri, M. Zaharuddin
{"title":"Virtual Reality based Mobile Robot Navigation in Greenhouse Environment","authors":"M. Azimi, Z. Shukri, M. Zaharuddin","doi":"10.1017/S2040470017001078","DOIUrl":"https://doi.org/10.1017/S2040470017001078","url":null,"abstract":"","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"87 1","pages":"854-859"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73129008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-01DOI: 10.1017/S2040470017000206
M. Dyrmann, R. Jørgensen, H. Midtiby
This paper presents a method for automating weed detection in colour images despite heavy leaf occlusion. A fully convolutional neural network is used to detect the weeds. The network is trained and validated on a total of more than 17,000 annotations of weeds in images from winter wheat fields, which have been collected using a camera mounted on an all-terrain vehicle. Hereby, the network is able to automatically detect single weed instances in cereal fields despite heavy leaf occlusion.
{"title":"RoboWeedSupport - Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network","authors":"M. Dyrmann, R. Jørgensen, H. Midtiby","doi":"10.1017/S2040470017000206","DOIUrl":"https://doi.org/10.1017/S2040470017000206","url":null,"abstract":"This paper presents a method for automating weed detection in colour images despite heavy leaf occlusion. A fully convolutional neural network is used to detect the weeds. The network is trained and validated on a total of more than 17,000 annotations of weeds in images from winter wheat fields, which have been collected using a camera mounted on an all-terrain vehicle. Hereby, the network is able to automatically detect single weed instances in cereal fields despite heavy leaf occlusion.","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"5 1","pages":"842-847"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73354586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-01DOI: 10.1017/S2040470017001121
J. Serrano, S. Shahidian, J. M. Silva, F. Moral, F. J. Rebollo
Proximal sensing for monitoring the productivity of a permanent Mediterranean pasture: influence of rainfall patterns J Serrano, S Shahidian, J Marques da Silva, F Moral and F Rebollo Departamento de Engenharia Rural, Instituto de Ciências Agrárias e Ambientais Mediterrânicas (ICAAM), Escola de Ciências e Tecnologia, Universidade de Évora, Apartado 94, 7002-554 Évora, Portugal, Departamento de Expresión Gráfica, Universidad de Extremadura, Badajoz, Spain jmrs@uevora.pt
{"title":"Proximal sensing for monitoring the productivity of a permanent Mediterranean pasture: influence of rainfall patterns","authors":"J. Serrano, S. Shahidian, J. M. Silva, F. Moral, F. J. Rebollo","doi":"10.1017/S2040470017001121","DOIUrl":"https://doi.org/10.1017/S2040470017001121","url":null,"abstract":"Proximal sensing for monitoring the productivity of a permanent Mediterranean pasture: influence of rainfall patterns J Serrano, S Shahidian, J Marques da Silva, F Moral and F Rebollo Departamento de Engenharia Rural, Instituto de Ciências Agrárias e Ambientais Mediterrânicas (ICAAM), Escola de Ciências e Tecnologia, Universidade de Évora, Apartado 94, 7002-554 Évora, Portugal, Departamento de Expresión Gráfica, Universidad de Extremadura, Badajoz, Spain jmrs@uevora.pt","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"25 1","pages":"796-801"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73910867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-01DOI: 10.1017/S2040470017001406
O. Krikeb, V. Alchanatis, O. Crane, A. Naor
{"title":"Evaluation of apple flowering intensity using color image processing for tree specific chemical thinning","authors":"O. Krikeb, V. Alchanatis, O. Crane, A. Naor","doi":"10.1017/S2040470017001406","DOIUrl":"https://doi.org/10.1017/S2040470017001406","url":null,"abstract":"","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"35 1","pages":"466-470"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85416627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-01DOI: 10.1017/S2040470017000474
J. Holland, D. Cammarano, G. Poile, M. Conyers
Potassium (K) is a macronutrient which plays a vital role on crop growth and metabolism. After N the requirements for K are greatest for most arable crops and so the availability of K is of critical importance to optimise production. The precision nutrient management of arable crops requires accurate and timely assessment of crop nutrient status. Much research and practice has focused on crop N status, while there has been a lack of focus on other important nutrients such as K. Therefore, in this study we assess the robustness of 12 fluorescence channels and several indices to predict nutrient status (K, Mg and Ca) across two cereal crops with different row management and lime status on an acidic K deficient soil. A multi-factorial experiment was used with the following treatment factors: crop (barley, wheat), K fertilizer rates (0, 25, 50, 100 kg K/ ha), lime (nil, 1 t/ ha) and two management factors (inter-row, windrow). At flowering the crop was sampled for biomass and nutrient content and proximal sensing (using a Multiplex fluorometer) undertaken of the crop canopy. Crop variables showed significant treatment effects. For instance, all crop variables were greater under the windrow treatment than the inter-row, K rate significantly increased grain yield and TGW, but K rate decreased protein and grain Ca and Mg content, also the grain yield was significantly greater under lime compared with the nil treatment. These crop effects enabled the identification of significant crop-fluorescence relationships. For instance, SFR_R (a chlorophyll index) predicted crop biomass (regardless of crop species) and FLAV predicted with the grain protein of windrow-grown barley. These results are promising and suggest crop-fluorescence relationships can be used to inform crop nutrient status which could be used to aid management decisions. Thus, there is good potential for fluorescence sensing to quantify crop K status and the opportunity to improve the timing and precision of K management for application within a precision agriculture system.
钾是一种对作物生长和代谢起重要作用的常量营养元素。施氮后，大多数可耕地作物对钾的需求量最大，因此钾的可用性对优化生产至关重要。耕地作物养分的精准管理要求对作物养分状况进行准确、及时的评估。许多研究和实践都集中在作物氮状态上，而缺乏对其他重要营养物质如钾的关注。因此，在本研究中，我们评估了12个荧光通道和几个指标的稳健性，以预测在酸性缺钾土壤上两种不同行管理和石灰状态的谷类作物的营养状况(K, Mg和Ca)。采用多因子试验，分别采用作物(大麦、小麦)、施钾量(0、25、50、100 kg K/ ha)、石灰(0、1 t/ ha)和两种管理因素(行间、窗)。在开花时，对作物进行生物量和养分含量取样，并对作物冠层进行近端感知(使用多重荧光计)。作物变量表现出显著的处理效果。例如，窗下处理的所有作物变量均大于行间处理，施钾量显著提高了籽粒产量和总重，但降低了籽粒蛋白质和钙、镁含量，石灰处理的籽粒产量显著高于无处理。这些作物效应使鉴定出显著的作物-荧光关系成为可能。例如，SFR_R(叶绿素指数)可以预测作物生物量(与作物种类无关)，而FLAV可以预测青稞的籽粒蛋白。这些结果是有希望的，并且表明作物荧光关系可以用来了解作物的营养状况，这可以用来帮助管理决策。因此，荧光传感在量化作物钾状态方面具有良好的潜力，并有机会改善精准农业系统中应用钾管理的时机和精度。
{"title":"The prediction of crop biomass, grain yield and grain quality using fluorescence sensing in cereals","authors":"J. Holland, D. Cammarano, G. Poile, M. Conyers","doi":"10.1017/S2040470017000474","DOIUrl":"https://doi.org/10.1017/S2040470017000474","url":null,"abstract":"Potassium (K) is a macronutrient which plays a vital role on crop growth and metabolism. After N the requirements for K are greatest for most arable crops and so the availability of K is of critical importance to optimise production. The precision nutrient management of arable crops requires accurate and timely assessment of crop nutrient status. Much research and practice has focused on crop N status, while there has been a lack of focus on other important nutrients such as K. Therefore, in this study we assess the robustness of 12 fluorescence channels and several indices to predict nutrient status (K, Mg and Ca) across two cereal crops with different row management and lime status on an acidic K deficient soil. A multi-factorial experiment was used with the following treatment factors: crop (barley, wheat), K fertilizer rates (0, 25, 50, 100 kg K/ ha), lime (nil, 1 t/ ha) and two management factors (inter-row, windrow). At flowering the crop was sampled for biomass and nutrient content and proximal sensing (using a Multiplex fluorometer) undertaken of the crop canopy. Crop variables showed significant treatment effects. For instance, all crop variables were greater under the windrow treatment than the inter-row, K rate significantly increased grain yield and TGW, but K rate decreased protein and grain Ca and Mg content, also the grain yield was significantly greater under lime compared with the nil treatment. These crop effects enabled the identification of significant crop-fluorescence relationships. For instance, SFR_R (a chlorophyll index) predicted crop biomass (regardless of crop species) and FLAV predicted with the grain protein of windrow-grown barley. These results are promising and suggest crop-fluorescence relationships can be used to inform crop nutrient status which could be used to aid management decisions. Thus, there is good potential for fluorescence sensing to quantify crop K status and the opportunity to improve the timing and precision of K management for application within a precision agriculture system.","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"139 1","pages":"172-177"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80408593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-01DOI: 10.1017/S2040470017001388
H. Alvemar, H. Andersson, H. Pedersen
Controlled traffic farming (CTF) systems aim to reduce soil compaction by restricting machinery field traffic to permanent traffic lanes. Grass-clover silage production is generally associated with intensive field traffic, resulting in reduced silage clover content. If CTF can increase yield and clover content in grass-clover leys, this would reduce the need for grain and expensive protein concentrate in dairy cow feed rations. A mixed integer programming model was developed to evaluate the potential profitability of CTF in a dairy farm context. Existing field trial data were used to calculate the expected yield outcome of CTF, based on reductions in trafficked area. The results revealed that CTF increased profitability by up to €50/ha. Total machinery costs are likely to increase on converting to CTF, but variable machinery costs are likely to decrease.
{"title":"Profitability of controlled traffic in grass silage production – economic modelling and machinery systems","authors":"H. Alvemar, H. Andersson, H. Pedersen","doi":"10.1017/S2040470017001388","DOIUrl":"https://doi.org/10.1017/S2040470017001388","url":null,"abstract":"Controlled traffic farming (CTF) systems aim to reduce soil compaction by restricting machinery field traffic to permanent traffic lanes. Grass-clover silage production is generally associated with intensive field traffic, resulting in reduced silage clover content. If CTF can increase yield and clover content in grass-clover leys, this would reduce the need for grain and expensive protein concentrate in dairy cow feed rations. A mixed integer programming model was developed to evaluate the potential profitability of CTF in a dairy farm context. Existing field trial data were used to calculate the expected yield outcome of CTF, based on reductions in trafficked area. The results revealed that CTF increased profitability by up to €50/ha. Total machinery costs are likely to increase on converting to CTF, but variable machinery costs are likely to decrease.","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"30 1","pages":"749-753"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90244614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-01DOI: 10.1017/S2040470017001169
C. Dillon, J. Shockley, T. Mark
Recent technological progress in high-speed planting (HSP) warrants economic analysis of its potential. A whole farm optimization model of a 1000 ha Kentucky, USA corn and soybean operation finds that operating cost savings (labor, fuel, tractor repairs) and yield increases couple in recovering annual ownership costs of HSP technology. Changes in farm net returns are positive for all 12-row planter scenarios and all double speed cases for the 16-row planter but not for a 50% increase in speed with the 16-row planter. The greatest profit potential occurred when adopting the combination of HSP and variable rate application (VRA), with increased net returns of up to 6.57% compared to conventional speed no VRA for the 12-row planter.
{"title":"The sensitivity of economic gains from high-speed planting","authors":"C. Dillon, J. Shockley, T. Mark","doi":"10.1017/S2040470017001169","DOIUrl":"https://doi.org/10.1017/S2040470017001169","url":null,"abstract":"Recent technological progress in high-speed planting (HSP) warrants economic analysis of its potential. A whole farm optimization model of a 1000 ha Kentucky, USA corn and soybean operation finds that operating cost savings (labor, fuel, tractor repairs) and yield increases couple in recovering annual ownership costs of HSP technology. Changes in farm net returns are positive for all 12-row planter scenarios and all double speed cases for the 16-row planter but not for a 50% increase in speed with the 16-row planter. The greatest profit potential occurred when adopting the combination of HSP and variable rate application (VRA), with increased net returns of up to 6.57% compared to conventional speed no VRA for the 12-row planter.","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"22 1","pages":"662-667"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86714273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-01DOI: 10.1017/S204047001700084X
S. Gibson-Poole, S. Humphris, I. Toth, A. Hamilton
The rapid development of unmanned aerial vehicles (UAV) has resulted in these aircraft being much easier to operate via the use of portable computers or phones, using fully automated flight paths and at a ready to fly price point that’s within the financial reach of most consumers. UAVs are potentially very useful tools for farmers as they allow an overhead view of crops and field boundaries and although they are typically only equipped with commercial off-the-shelf (COTS) digital cameras, recent photogrammetry techniques allow the creation of orthorectified visual data as well as a digital elevation model of the observed scene. This paper investigates the effectiveness of using a UAV with dual COTS cameras, one un-modified and one modified to sense near infra-red (NIR) wavelengths of light, in order to identify the onset of disease within a trial crop of potatoes. The trial was composed of 2 plots of 16 drills containing 12 tubers that had been exposed to the blackleg disease-causing bacterial pathogen Pectobacterium atrosepticum in order to demonstrate best practise tuber storage and haulm destruction methods. 11 sets of aerial data were gathered between 27/5/2016 ~ 29/7/2016 and then compared with ground truth data collected on 14/7/2016. Visual analysis of the data could only detect the onset of disease and not the specific infection and resulted in a users accuracy (UA) of 83% and producers accuracy (PA) of 78% in detecting the onset of disease, with a total accuracy (TA) of 91% and Kappa coefficient (K) of 0.75. The building blocks of an automated classification routine have been constructed using pixel and object based image analysis (OBIA) methods, which have shown promising first results (UA 65%, PA 73%, TA 87%, K 0.61) but requires further refinement to achieve an equivalent level of accuracy as that of the visual analysis.
无人机(UAV)的快速发展导致这些飞机通过使用便携式电脑或手机更容易操作，使用全自动飞行路径，并在大多数消费者的经济能力范围内的价格点飞行。对于农民来说，无人机是潜在的非常有用的工具，因为它们可以俯瞰作物和田地边界，尽管它们通常只配备商用现货(COTS)数码相机，但最近的摄影测量技术允许创建正校正视觉数据以及观察场景的数字高程模型。本文研究了使用具有双COTS摄像机的无人机的有效性，其中一个未经修改，另一个修改以感知近红外(NIR)波长的光，以识别马铃薯试验作物中的疾病发作。试验采用2个地块，16个钻孔，12根块茎暴露于黑腿病致病菌萎败胸杆菌，以展示最佳的块茎储存和根茎破坏方法。在2016年5月27日~ 7月29日期间收集了11组航空数据，并与2016年7月14日收集的地面真实数据进行了比较。数据的可视化分析只能检测疾病的发病，而不能检测特定的感染，检测疾病发病的用户准确率(UA)为83%，生产者准确率(PA)为78%，总准确率(TA)为91%，Kappa系数(K)为0.75。使用像素和基于对象的图像分析(OBIA)方法构建了自动分类例程的构建块，这些方法已经显示出有希望的初步结果(UA 65%， PA 73%， TA 87%， K 0.61)，但需要进一步改进以达到与视觉分析相同的精度水平。
{"title":"Identification of the onset of disease within a potato crop using a UAV equipped with un-modified and modified commercial off-the-shelf digital cameras","authors":"S. Gibson-Poole, S. Humphris, I. Toth, A. Hamilton","doi":"10.1017/S204047001700084X","DOIUrl":"https://doi.org/10.1017/S204047001700084X","url":null,"abstract":"The rapid development of unmanned aerial vehicles (UAV) has resulted in these aircraft being much easier to operate via the use of portable computers or phones, using fully automated flight paths and at a ready to fly price point that’s within the financial reach of most consumers. UAVs are potentially very useful tools for farmers as they allow an overhead view of crops and field boundaries and although they are typically only equipped with commercial off-the-shelf (COTS) digital cameras, recent photogrammetry techniques allow the creation of orthorectified visual data as well as a digital elevation model of the observed scene. This paper investigates the effectiveness of using a UAV with dual COTS cameras, one un-modified and one modified to sense near infra-red (NIR) wavelengths of light, in order to identify the onset of disease within a trial crop of potatoes. The trial was composed of 2 plots of 16 drills containing 12 tubers that had been exposed to the blackleg disease-causing bacterial pathogen Pectobacterium atrosepticum in order to demonstrate best practise tuber storage and haulm destruction methods. 11 sets of aerial data were gathered between 27/5/2016 ~ 29/7/2016 and then compared with ground truth data collected on 14/7/2016. Visual analysis of the data could only detect the onset of disease and not the specific infection and resulted in a users accuracy (UA) of 83% and producers accuracy (PA) of 78% in detecting the onset of disease, with a total accuracy (TA) of 91% and Kappa coefficient (K) of 0.75. The building blocks of an automated classification routine have been constructed using pixel and object based image analysis (OBIA) methods, which have shown promising first results (UA 65%, PA 73%, TA 87%, K 0.61) but requires further refinement to achieve an equivalent level of accuracy as that of the visual analysis.","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"15 1","pages":"812-816"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89015063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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