Advances in Animal Biosciences最新文献_第6页

Estimating pasture biomass with active optical sensors 利用主动光学传感器估算牧草生物量

Advances in Animal Biosciences Pub Date : 2017-07-01 DOI: 10.1017/S2040470017000838

K. Andersson, M. Trotter, A. Robson, D. Schneider, Lucy Frizell, Ashley Saint, D. Lamb, C. Blore

引用次数: 19

Comparing efficiency of different sampling schemes to estimate yield and quality parameters in fruit orchards 比较不同抽样方案估算果园产量和品质参数的效率

Advances in Animal Biosciences Pub Date : 2017-07-01 DOI: 10.1017/S2040470017000978

J. Arnó, J. A. Martínez-Casasnovas, A. Uribeetxebarria, A. Escolà, J. R. Rosell-Polo

引用次数: 8

Potential of on-board colour imaging for in-field detection and counting of grape bunches at early fruiting stages 车载彩色成像技术在葡萄早期结实阶段的田间检测和计数的潜力

Advances in Animal Biosciences Pub Date : 2017-07-01 DOI: 10.1017/S2040470017001030

F. Abdelghafour, B. Keresztes, C. Germain, J.-P. Da Costa

引用次数: 6

Interactions between landscape defined management zones and grazing management systems 景观界定的管理区和放牧管理系统之间的相互作用

Advances in Animal Biosciences Pub Date : 2017-07-01 DOI: 10.1017/S204047001700098X

E. Pena‐Yewtukhiw, D. Mata‐Padrino, J. Grove

引用次数: 3

Connecting crop models with highly resolved sensor observations to improve site-specific fertilisation 将作物模型与高分辨率传感器观测相结合，以改善特定地点的施肥

Advances in Animal Biosciences Pub Date : 2017-07-01 DOI: 10.1017/S2040470017000358

E. Wallor, K. Kersebaum, K. Lorenz, R. Gebbers

引用次数: 5

Using Unmanned Aircraft Systems for Early Detection of Soybean Diseases 利用无人机系统对大豆病害进行早期检测

Advances in Animal Biosciences Pub Date : 2017-07-01 DOI: 10.1017/S2040470017001315

C. Brodbeck, E. Sikora, D. Delaney, G. Pate, J. Johnson

{"title":"Using Unmanned Aircraft Systems for Early Detection of Soybean Diseases","authors":"C. Brodbeck, E. Sikora, D. Delaney, G. Pate, J. Johnson","doi":"10.1017/S2040470017001315","DOIUrl":"https://doi.org/10.1017/S2040470017001315","url":null,"abstract":"As the interest in Unmanned Aerial Systems (UAS) has increased, so has the interest in the application of these systems for use in agriculture. A variety of sensors, including Multi-Spectral, Near-Infrared, Thermal, and True-Color have the potential to benefit farmers when mounted to a UAS. But as this is an emerging field, there is little data available to demonstrate their use for early detection of plant diseases in crop production. In 2016, a preliminary study was launched to examine the potential of using aerial imagery from UAS to detect diseases in soybean crops. Two irrigated fields in Alabama were selected: Experiment 1, a 50-hectare field, and Experiment 2, a 5-hectare field. Each trial consisted of replicated plots using two foliar fungicide treatments and an untreated control. Aerial imagery (multi-spectral and true-color) was collected on a biweekly basis during this study. Using multi-spectral imagery, both the Normalized Difference Vegetative Index (NDVI) and Normalized Difference Red Edge Index (NDRE) were generated and compared to direct observations in the field. Disease severity of soybean rust, charcoal rot and Cercospora leaf blight were monitored on a biweekly basis and correlated to the UAS imagery. Preliminary results indicated plant stress can be detected using UAS imagery. In Experiment 1, stress associated with charcoal rot was visible in the NDRE imagery. This was of interest because at the time of flight, while it was noted that plants were yellowing, the root and stem disease itself had not been identified by direct observation. In Experiment 2, soybean rust was observed by direct observation and in both the NDRE and NDVI imagery. Soybean rust did have a negative impact on yield in Experiment 2, however severe drought conditions may have negated the yield loss likely caused by the development of charcoal rot in Experiment 1.","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"133 1","pages":"802-806"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80745848","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}

引用次数: 12

Using ancillary yield data to improve sampling and grape yield estimation of the current season 利用辅助产量数据改进当季的采样和葡萄产量估算

Advances in Animal Biosciences Pub Date : 2017-07-01 DOI: 10.1017/S2040470017000656

M. Araya-Almán, C. Acevedo-Opazo, S. Guillaume, H. Valdés-Gómez, N. Verdugo-Vásquez, Y. Moreno, B. Tisseyre

{"title":"Using ancillary yield data to improve sampling and grape yield estimation of the current season","authors":"M. Araya-Almán, C. Acevedo-Opazo, S. Guillaume, H. Valdés-Gómez, N. Verdugo-Vásquez, Y. Moreno, B. Tisseyre","doi":"10.1017/S2040470017000656","DOIUrl":"https://doi.org/10.1017/S2040470017000656","url":null,"abstract":"This paper proposes a methodology aiming at using historical yield data to improve yield sampling and yield estimation. The sampling method is based on a collaboration between historical data (at least three years) and yield measurements of the year performed on some sites within the field. It assumes a temporal stability of within field yield spatial patterns over the years. The first factor of a principal component analysis (PCA) is used to summarize the stable temporal patterns of within field yield data and it represents a large part of the variability of the different years assuming yield temporal stability and a high positive correlation between this factor and the yield. This main factor is then used to choose the best sites to sample (target sampling). Yield measurements are then used to calibrate a model that relates yield values to coordinates on the first factor of the PCA. This sampling method was tested on three vine fields (Vitis vinifera L.) in Chile and France with different varieties (Chardonnay, Cabernet Sauvignon and Syrah). For each of these fields, yield data of several years were available at the within field level. After temporal stability of yield patterns was verified for almost all the fields, the proposed sampling method was applied. Results were compared to those of a classical random sampling method showing that the use of historical yield data allows sampling sites selection to be optimized. Errors in yield estimations were reduced by more than 10% in all the cases, except when yield stable patterns are affected by specific events, i.e. early frost occurring on Chardonnay field.","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"58 1","pages":"515-519"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84129087","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}

引用次数: 5

Agronōmics: transforming crop science through digital technologies Agronōmics:通过数字技术改变作物科学

Advances in Animal Biosciences Pub Date : 2017-07-01 DOI: 10.1017/S2040470017001029

R. Sylvester-Bradley, D. Kindred, B. Marchant, Sebastian Rudolph, S. Roques, A. Calatayud, S. Clarke, Vincent Gillingham

引用次数: 6

Multispectral band selection for imaging sensor design for vineyard disease detection: case of Flavescence Dorée 多光谱波段选择用于葡萄园病害检测的成像传感器设计——以黄萎病为例

Advances in Animal Biosciences Pub Date : 2017-07-01 DOI: 10.1017/S2040470017000802

H. Al-Saddik, J. Simon, O. Brousse, F. Cointault

引用次数: 10

Spatiotemporal stability of management zones in a table grapes vineyard in Greece 希腊一个鲜食葡萄葡萄园管理区的时空稳定性

Advances in Animal Biosciences Pub Date : 2017-07-01 DOI: 10.1017/S2040470017000632

E. Anastasiou, Z. Tsiropoulos, T. Balafoutis, S. Fountas, C. Templalexis, D. Lentzou, G. Xanthopoulos

引用次数: 6