Advances in Animal Biosciences最新文献

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}