RESUMEN
Body condition evaluation is a common tool to assess energy reserves of dairy cows and to estimate their fatness or thinness. This study presents a computer-vision tool that automatically estimates cow's body condition score. Top-view images of 151 cows were collected on an Israeli research dairy farm using a digital still camera located at the entrance to the milking parlor. The cow's tailhead area and its contour were segmented and extracted automatically. Two types of features of the tailhead contour were extracted: (1) the angles and distances between 5 anatomical points; and (2) the cow signature, which is a 1-dimensional vector of the Euclidean distances from each point in the normalized tailhead contour to the shape center. Two methods were applied to describe the cow's signature and to reduce its dimension: (1) partial least squares regression, and (2) Fourier descriptors of the cow signature. Three prediction models were compared with manual scores of an expert. Results indicate that (1) it is possible to automatically extract and predict body condition from color images without any manual interference; and (2) Fourier descriptors of the cow's signature result in improved performance (R(2)=0.77).
Asunto(s)
Algoritmos , Crianza de Animales Domésticos , Bovinos/fisiología , Procesamiento de Imagen Asistido por Computador , Animales , Bovinos/anatomía & histología , Análisis de los Mínimos CuadradosRESUMEN
Achieving high quality wine grapes depends on the ability to maintain mild to moderate levels of water stress in the crop during the growing season. This study investigates the use of thermal imaging for monitoring water stress. Experiments were conducted on a wine-grape (Vitis vinifera cv. Merlot) vineyard in northern Israel. Irrigation treatments included mild, moderate, and severe stress. Thermal and visible (RGB) images of the crop were taken on four days at midday with a FLIR thermal imaging system and a digital camera, respectively, both mounted on a truck-crane 15 m above the canopy. Aluminium crosses were used to match visible and thermal images in post-processing and an artificial wet surface was used to estimate the reference wet temperature (T(wet)). Monitored crop parameters included stem water potential (Psi(stem)), leaf conductance (g(L)), and leaf area index (LAI). Meteorological parameters were measured at 2 m height. CWSI was highly correlated with g(L) and moderately correlated with Psi(stem). The CWSI-g(L) relationship was very stable throughout the season, but for that of CWSI-Psi(stem) both intercept and slope varied considerably. The latter presumably reflects the non-direct nature of the physiological relationship between CWSI and Psi(stem). The highest R(2) for the CWSI to g(L) relationship, 0.91 (n=12), was obtained when CWSI was computed using temperatures from the centre of the canopy, T(wet) from the artificial wet surface, and reference dry temperature from air temperature plus 5 degrees C. Using T(wet) calculated from the inverted Penman-Monteith equation and estimated from an artificially wetted part of the canopy also yielded crop water-stress estimates highly correlated with g(L) (R(2)=0.89 and 0.82, respectively), while a crop water-stress index using 'theoretical' reference temperatures computed from climate data showed significant deviations in the late season. Parameter variability and robustness of the different CWSI estimates are discussed. Future research should aim at developing thermal imaging into an irrigation scheduling tool applicable to different crops.
Asunto(s)
Productos Agrícolas/fisiología , Temperatura , Vitis/fisiología , Agua/análisis , Aire , Análisis de Varianza , Clima , Productos Agrícolas/efectos de la radiación , Hojas de la Planta/fisiología , Hojas de la Planta/efectos de la radiación , Tallos de la Planta/efectos de la radiación , Radiación , Análisis de Regresión , Estaciones del Año , Suelo , Vitis/efectos de la radiaciónRESUMEN
Canopy temperature has long been recognized as an indicator of plant water status and as a potential tool for irrigation scheduling. In the present study, the potential of using thermal images for an in-field estimation of the water status of cotton under a range of irrigation regimes was investigated. Thermal images were taken with a radiometric infrared video camera. Specific leaves that appeared in the camera field of view were sampled, their LWP was measured and their temperature was calculated from the images. Regression models were built in order to predict LWP according to the crop canopy temperature and to the empirical formulation of the crop water stress index (CWSI). Statistical analysis revealed that the relationship between CWSI and LWP was more stable and had slightly higher correlation coefficients than that between canopy temperature and LWP. The regression models of LWP against CWSI and against leaf temperatures were used to create LWP maps. The classified LWP maps showed that there was spatial variability in each treatment, some of which may be attributed to the difference between sunlit and shaded leaves. The distribution of LWP in the maps showed that irrigation treatments were better distinguished from each other when the maps were calculated from CWSI than from leaf temperature alone. Furthermore, the inclusion of the spatial pattern in the classification enhanced the differences between the treatments and was better matched to irrigation amounts. Optimal determination of the water status from thermal images should be based on an overall view of the physical status as well as on the analysis of the spatial structure. Future study will involve investigating the robustness of the models and the potential of using water status maps, derived from aerial thermal images, for irrigation scheduling and variable management in commercial fields.