Detection of the tulip breaking virus (TBV) in tulips using optical sensors

The tulip breaking virus (TBV) causes severe economic losses for countries that export tulips such as the Netherlands. Infected plants have to be removed from the field as soon as possible. There is an urgent need for a rapid and objective method of screening. In this study, four proximal optical sensing techniques for the detection of TBV in tulip plants were evaluated and compared with a visual assessment by crop experts as well as with an ELISA (enzyme immunoassay) analysis of the same plants. The optical sensor techniques used were an RGB color camera, a spectrophotometer measuring from 350 to 2500 nm, a spectral imaging camera covering a spectral range from 400 to 900 nm and a chlorophyll fluorescence imaging system that measures the photosynthetic activity. Linear discriminant classification was used to compare the results of these optical techniques and the visual assessment with the ELISA score. The spectral imaging system was the best optical technique and its error was only slightly larger than the visual assessment error. The experimental results appear to be promising, and they have led to further research to develop an autonomous robot for the detection and removal of diseased tulip plants in the open field. The application of this robot system will reduce the amount of insecticides and the considerable pressure on labor for selecting diseased plants by the crop expert. © 2010 The Author(s

High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion

Background: Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. Results: The models with a single color feature from RGB images predicted chlorophyll content with R2 ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R2 ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R2 of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R2 of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy. Conclusion: All three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum

High-throughput phenotyping of plant leaf morphological, physiological, and biochemical traits on multiple scales using optical sensing

Acquisition of plant phenotypic information facilitates plant breeding, sheds light on gene action, and can be applied to optimize the quality of agricultural and forestry products. Because leaves often show the fastest responses to external environmental stimuli, leaf phenotypic traits are indicators of plant growth, health, and stress levels. Combination of new imaging sensors, image processing, and data analytics permits measurement over the full life span of plants at high temporal resolution and at several organizational levels from organs to individual plants to field populations of plants. We review the optical sensors and associated data analytics used for measuring morphological, physiological, and biochemical traits of plant leaves on multiple scales. We summarize the characteristics, advantages and limitations of optical sensing and data-processing methods applied in various plant phenotyping scenarios. Finally, we discuss the future prospects of plant leaf phenotyping research. This review aims to help researchers choose appropriate optical sensors and data processing methods to acquire plant leaf phenotypes rapidly, accurately, and cost-effectively

Development of new non-destructive imaging techniques for estimating crop growth and nutrient status

University of Technology Sydney. Faculty of Engineering and Information Technology.Leaf dimensions and pigments are the important traits in plants that play a key role in estimating light interception, absorption and food production. In predictive research, these parameters are a useful data source for devising crop management techniques such as cultivation, pruning and fertilisation. Destructive and non-destructive techniques are commonly used for estimating crop growth and nutrient status. Although, destructive methods are more accurate, these are expensive, laborious and impracticable for large fields. In contrast, various non-destructive techniques have been developed for predicting crop N requirements that are relatively fast and less expensive. However, lack of consistency in accurately predicting the true N levels of different crop species under variable environments require further exploration of this area. In the present study, a new and relatively more efficient technique has been proposed for measuring leaf dimensions, chlorophyll, and N and phosphorus (P) content. In the initial study, leaf images from a range of plant species were collected using a handheld portable digital scanner (Pico Life). Edge detection and filtering algorithms were applied to identify the leaf section of the image against the background. Data of forty leaves that vary in shape and size (from grasses to broad leaf plant species) were collected and processed using a new algorithm as well as the Li-Cor 3100. Data indicated high accuracy of the proposed algorithm for estimating leaf area, length, width and perimeter. It was verified by a strong correlation (R²=0.999) between leaf area measured by Li-Cor 3100 and by digital scanner. After successful application of the digital scanner for estimating leaf size and dimensions, the images collected by this scanner were used for predicting chlorophyll, P and N content of tomato, broccoli and lettuce leaves. The plants were grown under controlled conditions using nutrient solution and at early reproductive growth (after 8 weeks of growth) these were exposed to various N levels for seven weeks. Data on leaf chlorophyll and N content were collected through biochemical assays (LabChl). In addition, data were collected by the SPAD-502 and portable scanner. Images collected by the portable scanner were processed by averaging the R (red), G (green) and B (blue) values of all the leaf pixels. Based on the RGB values, a new algorithm was developed that estimates leaf chlorophyll content (ChlRGB). Despite slight variations in response to applied N levels in the three crops, the leaf chlorophyll and N content significantly increased with increasing N levels in nutrient solution in the studied crop species. Under N deficient conditions (N0), tomato and broccoli plants showed significantly lower leaf N content just 2 weeks after treatment (WAT), compared with N-treated plants (any N level) suggesting a rapid response of these crops to N deficiency. However, response to various N levels in lettuce was slower and the difference in N concentrations in the leaves of N-deficient (0 and 0.2 N) and N-treated plants became significant at 5 WAT. Compared with leaf N, reduction in leaf chlorophyll levels in response to N deficiency was slow, and the difference in leaf chlorophyll content of N-deficient and N-sufficient plants was significant at 5 WAT in all the studied crops. The chlorophyll values calculated by SPAD and by the modified RGB technique were plotted against LabChl and N content. The correlation coefficient (R²) between SPAD values and LabChl was 0.90, 0.73 and 0.81 for tomato, lettuce and broccoli, respectively. In contrast, the relationship between ChlRGB and LabChl was relatively stronger and more consistent for all three crop species that is 0.97, 0.90 and 0.91 for tomato, lettuce and broccoli, respectively. Similarly, highly significant relationships (R² values) were recorded between the leaf N content and ChlRGB such as 0.94, 0.93 and 0.72 for broccoli, tomato and lettuce, respectively. The high accuracy of the modified RGB technique for measuring the crop N and chlorophyll content was further confirmed by field-based studies. This technique again outperformed the SPAD-502 in estimating leaf chlorophyll content. For example, R² values for SPAD readings and LabChl were 0.90, 0.92 and 0.84 for broccoli, tomato and lettuce, respectively. The efficiency of this modified RGB technique was also tested against dark green colour index (DGCI), a commonly used algorithm for estimating leaf chlorophyll and N. The result indicated that the modified RGB technique outperformed DGCI in the precision of predicting leaf Chl levels. A separate study was conducted to estimate N requirements of field-grown cotton using the modified RGB technique, where the efficiency of this technique was compared with other non-destructive methods. The crop was grown under various N levels, and leaf N concentrations were measured at different growth stages; late vegetative, peak reproductive and late reproductive growth phase. The data showed that the modified RGB technique was more effective and accurate in estimating cotton leaf N status compared with the SPAD-502 as well as other handheld crop sensor. In the final experiment, the leaf P and anthocyanin levels of different crops such as cotton, tomato and lettuce was estimated using the modified RGB technique. The plants were grown under on different P concentrations. Leaf chlorophyll anthocyanin and P content were measured using laboratory techniques, while leaf images were collected by the handheld crop sensor. Using RGB values of the collected images, leaf area, leaf perimeter and chlorophyll content were calculated. These data were further used to train a linear discriminant analysis (LDA) classifier for estimating leaf anthocyanin and P content. In addition, a decision tree model was used to classify cotton plants into different groups containing variable P levels. Both LDA and decision tree models successfully classified these plants on the basis of leaf P content, indicating that P deficiency in crop plants can be predicted using morphological data. It also suggested that the modified RGB technique is highly efficient in estimating P requirements in different crop species

Käytön arvioimiseksi digitaalisen kuvantamisen arvioida kasvuun nuorten maniokki

The world population is growing and is expected to reach over 9 billion in about 30 years. Climate change is also widely expected to worsen famines in certain regions of the world. This will drastically increase global food demand. Food security efforts should be therefore be geared towards promoting food crops that can thrive in these regions and can withstand the condition likely to be brought about by changing climate. Cassava is a typical example of such a crop. This study investigated the use of digital images to estimate growth parameters of young cassava plants. Cassava was cultivated in pots at the University of Helsinki greenhouse at Viikki. The plants were given different water level (100%, 60% and 30% saturation) and potassium (0.1, 1.0, 4.0, 16.0 and 32.0mM) treatments. Digital red-green-blue (RGB) and multispectral images were taken every other week for 5 consecutive times. The images were processed to obtain leaf area, Normalized Difference Vegetation Index (NDVI), and Crop Senescence Index (CSI) and correlated with directly measured growth parameters of the young cassava crops. It was observed that leaf area that was computed from images, and NDVI which was computed from the multispectral images have significant positive correlations with the growth parameters, ie, actual leaf area, chlorophyll content, and plant biomass. CSI however showed weak a correlation between the growth parameters of the young cassava plants. Images leaf area and NDVI were then used to identify the changes in the effects of the water and potassium treatments

Red-Green-Blue and Multispectral Imaging as Potential Tools for Estimating Growth and Nutritional Performance of Cassava under Deficit Irrigation and Potassium Fertigation

Cassava has high energy value and rich nutritional content, yet its productivity in the tropics is seriously constrained by abiotic stresses such as water deficit and low potassium (K) nutrition. Systems that allow evaluation of genotypes in the field and greenhouse for nondestructive estimation of plant performance would be useful means for monitoring the health of plants for crop-management decisions. We investigated whether the red–green–blue (RGB) and multispectral images could be used to detect the previsual effects of water deficit and low K in cassava, and whether the crop quality changes due to low moisture and low K could be observed from the images. Pot experiments were conducted with cassava cuttings. The experimental design was a split-plot arranged in a completely randomized design. Treatments were three irrigation doses split into various K rates. Plant images were captured beginning 30 days after planting (DAP) and ended at 90 DAP when plants were harvested. Results show that biomass, chlorophyll, and net photosynthesis were estimated with the highest accuracy (R2 = 0.90), followed by leaf area (R2 = 0.76). Starch, energy, carotenoid, and cyanide were also estimated satisfactorily (R2 > 0.80), although cyanide showed negative regression coefficients. All mineral elements showed lower estimation accuracy (R2 = 0.14–0.48) and exhibited weak associations with the spectral indices. Use of the normalized difference vegetation index (NDVI), green area (GA), and simple ratio (SR) indices allowed better estimation of growth and key nutritional traits. Irrigation dose 30% of pot capacity enriched with 0.01 mM K reduced most index values but increased the crop senescence index (CSI). Increasing K to 16 mM over the irrigation doses resulted in high index values, but low CSI. The findings indicate that RGB and multispectral imaging can provide indirect measurements of growth and key nutritional traits in cassava. Hence, they can be used as a tool in various breeding programs to facilitate cultivar evaluation and support management decisions to avert stress, such as the decision to irrigate or apply fertilizers

Red-Green-Blue and Multispectral Imaging as Potential Tools for Estimating Growth and Nutritional Performance of Cassava under Deficit Irrigation and Potassium Fertigation

Cassava has high energy value and rich nutritional content, yet its productivity in the tropics is seriously constrained by abiotic stresses such as water deficit and low potassium (K) nutrition. Systems that allow evaluation of genotypes in the field and greenhouse for nondestructive estimation of plant performance would be useful means for monitoring the health of plants for crop-management decisions. We investigated whether the red–green–blue (RGB) and multispectral images could be used to detect the previsual effects of water deficit and low K in cassava, and whether the crop quality changes due to low moisture and low K could be observed from the images. Pot experiments were conducted with cassava cuttings. The experimental design was a split-plot arranged in a completely randomized design. Treatments were three irrigation doses split into various K rates. Plant images were captured beginning 30 days after planting (DAP) and ended at 90 DAP when plants were harvested. Results show that biomass, chlorophyll, and net photosynthesis were estimated with the highest accuracy (R2 = 0.90), followed by leaf area (R2 = 0.76). Starch, energy, carotenoid, and cyanide were also estimated satisfactorily (R2 > 0.80), although cyanide showed negative regression coefficients. All mineral elements showed lower estimation accuracy (R2 = 0.14–0.48) and exhibited weak associations with the spectral indices. Use of the normalized difference vegetation index (NDVI), green area (GA), and simple ratio (SR) indices allowed better estimation of growth and key nutritional traits. Irrigation dose 30% of pot capacity enriched with 0.01 mM K reduced most index values but increased the crop senescence index (CSI). Increasing K to 16 mM over the irrigation doses resulted in high index values, but low CSI. The findings indicate that RGB and multispectral imaging can provide indirect measurements of growth and key nutritional traits in cassava. Hence, they can be used as a tool in various breeding programs to facilitate cultivar evaluation and support management decisions to avert stress, such as the decision to irrigate or apply fertilizers

The spectral invariant approximation within canopy radiative transfer to support the use of the EPIC/DSCOVR oxygen B-band for monitoring vegetation

EPIC (Earth Polychromatic Imaging Camera) is a 10-channel spectroradiometer onboard DSCOVR (Deep Space Climate Observatory) spacecraft. In addition to the near-infrared (NIR, 780 nm) and the 'red' (680 nm) channels, EPIC also has the O2 A-band (764±0.2 nm) and B-band (687.75±0.2 nm). The EPIC Normalized Difference Vegetation Index (NDVI) is defined as the difference between NIR and 'red' channels normalized to their sum. However, the use of the O2 B-band instead of the 'red' channel mitigates the effect of atmosphere on remote sensing of surface reflectance because O2 reduces contribution from the radiation scattered by the atmosphere. Applying the radiative transfer theory and the spectral invariant approximation to EPIC observations, the paper provides supportive arguments for using the O2 band instead of the red channel for monitoring vegetation dynamics. Our results suggest that the use of the O2 B-band enhances the sensitivity of the top-of-atmosphere NDVI to the presence of vegetation.Shared Services Center NASAhttp://www.sciencedirect.com/science/article/pii/S0022407317300195First author draf

TLS-bridged co-prediction of tree-level multifarious stem structure variables from worldview-2 panchromatic imagery: a case study of the boreal forest

In forest ecosystem studies, tree stem structure variables (SSVs) proved to be an essential kind of parameters, and now simultaneously deriving SSVs of as many kinds as possible at large scales is preferred for enhancing the frontier studies on marcoecosystem ecology and global carbon cycle. For this newly emerging task, satellite imagery such as WorldView-2 panchromatic images (WPIs) is used as a potential solution for co-prediction of tree-level multifarious SSVs, with static terrestrial laser scanning (TLS) assumed as a ‘bridge’. The specific operation is to pursue the allometric relationships between TLS-derived SSVs and WPI-derived feature parameters, and regression analyses with one or multiple explanatory variables are applied to deduce the prediction models (termed as Model1s and Model2s). In the case of Picea abies, Pinus sylvestris, Populus tremul and Quercus robur in a boreal forest, tests showed that Model1s and Model2s for different tree species can be derived (e.g. the maximum R2 = 0.574 for Q. robur). Overall, this study basically validated the algorithm proposed for co-prediction of multifarious SSVs, and the contribution is equivalent to developing a viable solution for SSV-estimation upscaling, which is useful for large-scale investigations of forest understory, macroecosystem ecology, global vegetation dynamics and global carbon cycle.This work was financially supported in part by the National Natural Science Foundation of China [grant numbers 41471281 and 31670718] and in part by the SRF for ROCS, SEM, China. (41471281 - National Natural Science Foundation of China; 31670718 - National Natural Science Foundation of China; SRF for ROCS, SEM, China)http://www-tandfonline-com.ezproxy.bu.edu/doi/abs/10.1080/17538947.2016.1247473?journalCode=tjde20Published versio