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In viticulture, it is critical to predict productivity levels of the different vineyard zones to undertake appropriate cropping practices. To overcome this challenge, the final yield was predicted by combining vegetation indices (VIs) to sense the health status of the crop and by computer vision to obtain the vegetated fraction cover (Fc) as a measure of plant vigour. Multispectral imagery obtained from an unmanned aerial vehicle (UAV) is used to obtain VIs and Fc, which are used together with artificial neural networks (ANN) to model the relationship between VIs, Fc and yield. The proposed methodology was applied in a vineyard, where different irrigation and fertilisation doses were applied. The results showed that using computer vision techniques to differentiate between canopy and soil is necessary in precision viticulture to obtain accurate results. In addition, the combination of VIs (reflectance approach) and Fc (geometric approach) to predict vineyard yield results in higher accuracy (root mean square error (RMSE)?=?0.9 kg vine?1 and relative error (RE)?=?21.8% for the image when close to harvest) compared to the simple use of VIs (RMSE?=?1.2 kg vine?1 and RE?=?28.7%). The implementation of machine learning techniques resulted in much more accurate results than linear models (RMSE?=?0.5 kg vine?1 and RE?=?12.1%). More precise yield predictions were obtained when images were taken close to the harvest date, although promising results were obtained at earlier stages. Given the perennial nature of grapevines and the multiple environmental and endogenous factors determining yield, seasonal calibration for yield prediction is required.
相似文献Design Blood samples were collected from beef cattle in northern Queensland, the Northern Territory and northern Western Australia, and from dairy cattle in northern Queensland. The specificity of a serological test for JD was estimated by testing the blood samples with an absorbed ELISA kit. Further samples were collected from cattle with positive ELISA results to determine the presence or absence of infection with Mycobacterium avium subsp paratuberculosis .
Procedure During 1995 and 1996, blood, tissue and gut contents were collected from beef cattle at abattoirs in Queensland and the Northern Territory; and blood and faecal samples were collected from dairy cattle in herds assessed to be most at risk for JD in northern Queensland. The blood samples were tested using an absorbed ELISA kit. Tissues and gut contents from beef cattle that had positive ELISA results were cultured for M avium subsp paratuberculosis , and tissues were examined histo-logically. Faecal samples from dairy cattle with positive ELISA results were cultured for M avium subsp paratuberculosis .
Results Estimates of specificity for this absorbed ELISA in mature northern Australian cattle were 98.0% (97.0 to 98.8%, 95% CI) in beef cattle, and 98.3% (96.7 to 99.3%, 95% CI) in dairy cattle.
Conclusion Estimates of specificity in this study were lower for beef cattle from the Northern Territory and northern Western Australia and for dairy cattle from northern Queensland than those quoted from studies on cattle in southern Western Australia. This should be considered when serological testing using the JD ELISA is carried out on northern Australian cattle. 相似文献