基于RGB植被指数的大田油菜图像分割定量评价

以自然光下大田油菜幼苗图像为研究对象,运用超红指数ExR、超绿指数ExG、超绿超红差分指数ExGR、归一化植被指数NDI、植被提取颜色指数CIVE、植被指数组合COM等6种常用植被指数和阈值算法分割具有阴影区域的大田油菜图像,同时试验中引入定量评价标准客观评价常用RGB空间植被指数的分割效果。结果表明:定性分析中COM指数优于其他5种植被指数,能够减少阴影带来的分割影响,并在局部叶片分割试验中保留完整叶片轮廓;定量分析中COM指数提供最佳分割精度、灵敏度和特指度分别为94.1%、97.2%、90.9%,其相应标准差为1.1、1.3和0.06。     

A new color index for vegetation segmentation and classification

Color vegetation indices enable various precision agriculture applications by transforming a 3D-color image into its 1D-grayscale counterpart, such that the color of vegetation pixels can be accentuated, while those of nonvegetation pixels are attenuated. The quality of the transformation is essential to the outcomes of computational analyses to follow. The objective of this article is to propose a new vegetation index, the Elliptical Color Index (ECI), which leverages the quadratic discriminant analysis of 3D-color images along a normalized red (r)—green (g) plane. The proposed index is defined as an ellipse function of r and g variables with a shape parameter. For comparison, the ECI’s performance was evaluated along with six other indices, by using 240 color images as a test sample captured from four vegetation species under different illumination and background conditions, together with the corresponding ground-truth patterns. For comparative analysis, the receiver operating characteristic (ROC) and the precision–recall (PR) curves helped quantify the overall performance of vegetation segmentation across all of the vegetation indices evaluated. For a practical appraisal of vegetation segmentation outcomes, this paper applied Gaussian filtering, and then the thresholding method of Otsu, to the grayscale images transformed by each of the indices. Overall, the test results confirmed that ECI outperforms the other indices, in terms of the area under the curves of ROC and PR, as well as other performance metrics, including total error, precision, and F-score.

     

Comparing narrow and broad-band vegetation indices to estimate leaf chlorophyll content in planophile crop canopies

A comparison of the sensitivity of several broad- and narrow-band vegetation indices (VIs) to leaf chlorophyll content in planophile crop canopies is addressed by the analysis of a large synthetic dataset. Broad-band indices included classical slope-based VIs (i.e. NDVI—normalized difference VI and SR—simple ratio) and some indices incorporating green reflectance (i.e. Green NDVI, NIR/green ratio and the newly proposed CVI—chlorophyll vegetation index), whereas narrow-band indices included those specifically proposed to estimate leaf chlorophyll at the canopy scale (i.e. MCARI—modified chlorophyll absorption reflectance index, TCARI—transformed CARI, TCARI/OSAVI ratio—TCARI/optimized soil adjusted VI and REIP—red edge inflection position). Synthetic data were obtained from the coupled PROSPECT + SAILH leaf and canopy reflectance models in the direct mode. In addition to traditional regression-based statistics (coefficient of determination and root mean square error, RMSE), changes in sensitivity of a VI over the range of chlorophyll content were analyzed using a sensitivity function. The broad-band chlorophyll vegetation index outperformed the other VIs considered as a leaf chlorophyll estimator at the canopy scale, with the exception of the TCARI/OSAVI ratio for some soil conditions.     

基于Hyperion植被指数的干旱地区稀疏植被覆盖度估测

受稀疏植被与明亮土壤背景影响,干旱地区植被覆盖精确遥感估测难度较大。以Hyperion影像为数据源,选取甘肃省民勤绿洲-荒漠过渡带为研究区,系统比较了利用不同类型高光谱及多光谱植被指数估测干旱地区稀疏植被覆盖度的能力,以期确定干旱地区稀疏植被覆盖度估测的最佳植被指数。不同植被指数估测稀疏植被覆盖度的能力利用线性回归R2及留一交叉验证的均方根误差进行比较,结果表明:高光谱植被指数估测稀疏植被覆盖度的能力显著优于相应的多光谱植被指数,抗大气植被指数(ARVI)及抗土壤和大气植被指数(SARVI)表现明显优于归一化植被指数(NDVI)与土壤调节植被指数(SAVI),其中以基于833.3nm/640.5nm波段组合的ARVI表现最佳,R2可达0.7294,均方根误差(RMSE)仅为5.5488。     

Changes in Organic Carbon and Nutrient Contents of Highly Productive Paddy Soils in Yujiang County of Jiangxi Province, China and Their Environmental Application

Paddy field is an important land use in subtropical China. Development of high soil fertility and productivity is the management goal of paddy field. Fertilization and management practices have not only influenced the status of organic matter and nutrients in the soil but also affected the environmental quality. This article investigates the contents of organic carbon and the nutrients, and the change over the last 20 years in highly productive paddy soils and their environmental application. Field soils were sampled and the analytical results were compared with the corresponding values in the Second Soil Survey in Yujiang County of Jiangxi Province, China. The results showed that surface soils at a depth of 0-10 cm in highly productive paddy fields in Yujiang County of Jiangxi Province had contents of organic carbon (20.2±3.88) g kg⁻¹, total nitrogen (2.09±0.55) g kg⁻¹, and available phosphorus (42.7±32.7) mg kg⁻¹, respectively, which were all at very rich levels. Over the last 20 years, the organic carbon pool of the highly productive paddy soils reached a steady state. Total N and available P significantly increased, whereas available K changed a little. The amount and percentage of P immobilization in the surface soil (0-10 cm) of highly productive paddy fields were (142.7±41.1) mg kg⁻¹ and (36.2±10.4)% of added P, and CEC (7.93±1.32) cmol kg⁻¹. These two parameters were not higher than the mean values of paddy soils and upland red soils in the areas. Results also showed that fertilizer P in highly productive paddy soils had a high mobility and was prone to move toward a water body, which is the main source of nutrients causing eutrophication. Because of a weak K-fixing capacity, the available K content was not high in highly productive paddy soils. This suggests that attention should be paid to the K balance and the increase of soil K pool.     

Analysis of laser light propagation in kiwifruit using backscattering imaging and Monte Carlo simulation

The propagation of laser light in kiwifruit (Actinidia deliciosa) tissue was measured by backscattering imaging and modelled with the Monte Carlo (MC) method. The parameters of the vision system (8 bit/channel camera, 27.42 μm/pixel resolution) and the laser module (785 nm, 45 mW, Ø1 mm) were utilized in simulation. The required number of the photons was optimized with time-resolved MC model. The injected photon pulse travelled further than the beam radius and the calculated intensity fell below the noise level of the camera within 1 ns time. This short pulse contains 2.49 × 10⁸ photons and its application reduced computation load compared to the amount emitted within the integration time of 0.5–8.3 ms. The statistical effects of the optical properties of the tissue, scattering coefficient (μ_s) absorption coefficient (μ_a) and anisotropy factor (g), on photon flux was evaluated within ±20% range relative to expected mean values of μ_a = 0.9 cm⁻¹ and μ_s = 40 cm⁻¹. The anisotropy factor was taken into account using the Heyney–Greenstein phase function and was adjusted to g = 0.8 ± 20%. Because individual significance of each optical property was also analysed, scattering (μ_s) and transport corrected reduced scattering coefficients (μ^′_s=[1−g]μ_s) must be distinguished. The multi-factor ANOVA test pointed out the highest importance (p < 0.001) of the anisotropy factor amongst scattering and absorption coefficients.In the kiwi backscattering images, rotation of the intensity profiles was observed as a result of changing anisotropy. The measured and calculated profiles were compared to estimate the anisotropy factor of kiwifruits. Significant difference (p < 0.01) was found between anisotropy of premium quality and overripe pieces with respect to the fruit texture properties.     

Spectral and spatial differences in response of vegetation indices to nitrogen treatments on apple

Handheld chlorophyll sensors and remote sensing are two nondestructive approaches for estimating plant nitrogen (N) status, which are now commercially available. In this paper we address three questions on the application of these technologies in perennial fruit trees: (1) can individual leaf meter measurements be used to predict N status for surrounding trees?, (2) are narrow band indices more sensitive than the normalized difference vegetation index (NDVI) to differences in plant N?, and (3) is NDVI from satellite remote sensing correlated to leaf level vegetation indices? We evaluated data from a N rate trial conducted in a commercial Fuji apple orchard (Malus domestica Borkh. cv. ‘Fuji’). SPAD and CM1000 handheld chlorophyll meters and reflectance measurements using a portable spectrometer were made on individual leaves three or four times during each growing season. The reflectance measurements were used to determine NDVI and three narrow band vegetation indices. Satellite imagery from the Quickbird sensor was acquired two or three times during each growing season and used to generate NDVI for individual trees. The leaf meter measurements and vegetation indices were compared with the N application rate and plant N status measured as total leaf tissue N.We evaluated how well single leaf meter measurements predict N status for surrounding trees by calculating the differences between actual and estimated N applications from individual measurements. On average, a sample of 12 leaves (from the same treatment and same measurement date) resulted in an estimation error of 30 kg ha⁻¹ for either the SPAD or the CM1000 sensor, representing almost half of the range in N treatment rates. To evaluate any improvement in prediction of applied N using narrow band indices, we used analysis of variance (ANOVA) to compare three narrow band indices with the leaf meters and NDVI measured at leaf and canopy levels. Two narrow band indices, red edge vegetation stress index (RVSI) and modified chlorophyll absorption in reflectance index (MCARI) had higher F-values (31 and 41, respectively) than did NDVI from leaf level measurements (26), from satellite NDVI (6), or the CM1000 chlorophyll meter (12). The ANOVA results support improvements in leaf sensors using index values other than NDVI. We found that NDVI from satellite imagery acquired close to the leaf level measurement dates were positively correlated to the chlorophyll sensors and vegetation indices. When the data was averaged to the experiment plot level (twelve leaves total), the correlation coefficients between the satellite NDVI and the other sensors ranged from 0.68 for NDVI from leaf level reflectance to 0.84 with the CM1000 chlorophyll meter. Given the level of correlations, remote sensing might be a useful tool to extrapolate handheld measurements spatially throughout an orchard.     

Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques

Early and accurate diagnosis is a critical first step in mitigating losses caused by plant diseases. An incorrect diagnosis can lead to improper management decisions, such as selection of the wrong chemical application that could potentially result in further reduced crop health and yield. In tomato, initial disease symptoms may be similar even if caused by different pathogens, for example early lesions of target spot (TS) caused by the fungus Corynespora cassicola and bacterial spot (BS) caused by Xanthomonas perforans. In this study, hyperspectral imaging (380–1020 nm) was utilized in laboratory and field (collected by an unmanned aerial vehicle; UAV) settings to detect both diseases. Tomato leaves were classified into four categories: healthy, asymptomatic, early and late disease development stages. Thirty-five spectral vegetation indices (VIs) were calculated to select an optimum set of indices for disease detection and identification. Two classification methods were utilized: (i) multilayer perceptron neural network (MLP), and (ii) stepwise discriminant analysis (STDA). Best wavebands selection was considered in blue (408–420 nm), red (630–650 nm) and red edge (730–750 nm). The most significant VIs that could distinguish between healthy leaves and diseased leaves were the photochemical reflectance index (PRI) for both diseases, the normalized difference vegetation index (NDVI850) for BS in all stages, and the triangular vegetation index (TVI), NDVI850 and chlorophyll index green (Chl green) for TS asymptomatic, TS early and TS late disease stage respectively. The MLP classification method had an accuracy of 99%, for both BS and TS, under field (UAV-based) and laboratory conditions.

     

Effects of Chlorine Dioxide Gas on Postharvest Physiology and Storage Quality of Green Bell Pepper （Capsicum frutescens L. var. Longrum）

The effects of treatment of chlorine dioxide (ClO2) gas on postharvest physiology and preservation quality of green bell peppers were studied. Green bell peppers were collected in bags and treated with 0, 5, 10, 20, and 50 mg L-1 ClO2 gas at 10 ± 0.5℃ for over 40 d, and the changes in postharvest physiology and preservation quality of the peppers were evaluated during the storage. The inhibition of rot of the peppers was observed for all the tested ClO2 gas treatments. The rot rates of the treated samples were 50% lesser than those of the control after day 40 of storage. The highest inhibitory effect was obtained after 50 mg L-1 ClO2 gas treatment, where the peppers did not decay until day 30 and showed only one-fourth of the rot rate of the control at day 40 of storage. The respiratory activity of the peppers was significantly (P ＜ 0.05) inhibited by 20 and 50 mg L-1 ClO2 treatments, whereas no significant effects on respiratory activity were observed with 5 and 10 mg L-1ClO2 treatments (P＞ 0.05). Except for 50 mg L-1 ClO2, malondialdenyde (MDA) contents in the peppers treated with 5,10, or 20 mg L-1 ClO2 were not significantly (P＞0.05) different from those in the control. Degradation of chlorophyll in the peppers was delayed by 5 mg L-1 ClO2, but promoted by 10, 20, or 50 mg L-1 ClO2. The vitamin C content, titratable acidity,and total soluble solids of the peppers treated by all the tested ClO2 gas did not significantly change during the storage.The results suggested that ClO2 gas treatment effectively delayed the postharvest physiological transformation of green peppers, inhibited decay and respiration, maintained some nutritional and sensory quality, and retarded MDA accumulation.     

基于颜色指数与阈值法的稻田图像分割

为系统、全面地分析不同颜色指数对南方稻田图像分割的适应性,以分蘖期、拔节期稻田图像为研究对象,选择36种常用的颜色指数,采用Otsu阈值法开展基于颜色指数和阈值的图像分割研究,通过比较各颜色指数的分割结果,明确分蘖期和拔节期图像分割的主要干扰因素,筛选最适宜稻田图像分割的颜色指数。结果表明:水稻倒影、浮萍是分蘖期稻田图像分割的主要干扰因素,叶片镜面反射、浮萍和土壤阴影是拔节期稻田图像分割的主要干扰因素;组合指数COM2、MxEG、CIVE和GMR在分蘖期图像和拔节期图像均具有较好的分割精度。因此,基于颜色指数COM2、MxEG、CIVE、GMR和Otsu阈值的稻田图像分割方法对稻田图像分割的干扰要素具有较强的区分能力,分割精度较高,更适宜于南方稻田图像处理研究。