Optimal Wavebands Research Articles

Quantitative determination of oxides in cement raw meal based on near-infrared spectroscopy and hybrid feature selection strategy

Determining the content of components in cement raw meal is vital in Portland cement manufacturing. Near-infrared spectroscopy (NIRS)enables rapid, safe, and accurate quantitative analysis of the primary constituents of cement raw meal (CaO, SiO2, Al2O3, and Fe2O3), thereby assisting the cement industry in reducing the time delay in raw meal proportioning control and enhancing the quality of the raw meal. This study employed a hybrid feature selection strategy to identify effective feature points in the NIRS of raw meal samples. Effective feature points from the raw meal were integrated with partial least squares (PLS) to develop a regression model. Initially, NIRS data from raw meal samples were obtained using a Fourier NIR spectrometer. Subsequently, sample set partitioning based on joint X-Y distance (SPXY) was utilized to divide the samples into a calibration set (70 samples) and a validation set (22 samples). Next, A backward interval partial least squares (BiPLS) approach, along with various preprocessing algorithms, was then employed to initially screen the optimal waveband combinations of the NIRS. Within these combinations, effective feature points were further identified using competitive adaptive reweighted sampling (CARS), variable combined population analysis (VCPA) and weighted fusion variable space shrinkage approach (WFVSSA). Finally, we developed and compared PLS models based on the full spectrum and three hybrid feature selection methods. The results demonstrated that.WFVSSA offers a comprehensive assessment of wavelength importance, effectively preserving feature points while eliminating redundant information. BiPLS-WFVSSA-PLS outperformed BiPLS-CARS-PLS and BiPLS-VCPAPLS on the validation set, with R2, RMSEP, and RPD values of 0.8676, 0.1431, and 2.8129 for CaO; 0.9212, 0.1216, and 3.6461 for SiO2; 0.9198, 0.0607, and 3.5618 for Al2O3; and 0.8717, 0.0166, and 2.8614 for Fe2O3, respectively. These findings underscore the superior accuracy and stability of BiPLS-WFVSSA-PLS in determining cement raw meal component contents.

Identifying the optimal waveband positions for autumn grassland senescence detection using the broadband multispectral remotely sensed dataset

While remote sensing of grass senescence is addressed in the literature, knowledge of optimal waveband positions that are suitable for discriminating between senescent and non-senescent grasses is still limited. Notably, detection of senescent grass is important for understanding the available forage in rangeland environments and associated ecological implications. The free provision of remote sensing data from modern broadband multispectral sensors with improved spatial and spectral properties offers prospects for reliable and wall-to-wall monitoring of grassland senescence in rangeland ecosystems. The current study tested the potential of the modern multispectral remote sensing dataset (i.e., Sentinel 2 and Landsat) in mapping the senescent grass, and to identify the optimal waveband positions that are suitable for discriminating between senescent and non-senescent grasses. Locational information for both senescent and non-senescent grasses was acquired on the field and was used to train the classification process. A Random Forest classification approach was employed using the Landsat 8 and Sentinel 2 multispectral datasets to spectrally discern between senescent and non-senescent grasses. Our analysis yielded overall classification accuracies of 0.82 and 0.78 and kappa coefficients of 0.64 and 0.56 for Sentinel 2 and Landsat 8, respectively. Using the stepwise selection approach, the study further identified that the Red Edge Position (REP), and the visible green and red bands of the electromagnetic spectrum were the optimal waveband positions for separating between senescent and non-senescent grasses based on the broadband multispectral remote sensing. This study has demonstrated the value of the broadband multispectral remote sensing data in mapping autumn grassland senescence, and this lays a foundation for effective operational scale monitoring of foraging resources at the landscape scale, particularly during dry periods.

A new multispectral index for canopy nitrogen concentration applicable across growth stages in ryegrass and barley

Accurately monitoring Canopy Nitrogen Concentration (CNC) is a prerequisite for precision nitrogen (N) fertiliser management at the farm scale with carbon and N budgeting across the landscape and ecosystems. While many spectral indices have been proposed for CNC monitoring, their applicability and accuracy are often adversely affected by confounding factors such as aboveground biomass (AGB), crop type, growth stages, and environmental conditions, limiting their broader application and adoption; with AGB being one of the most dominant signals and confounding factors at canopy scale. The confounding effect can become more challenging as AGB is also physiologically linked with CNC across the growth stages. Additionally, the interplay between index form, selection of optimal wavebands and their bandwidths remains poorly understood for CNC index design. This study proposes robust and cost-effective 2- and 4-waveband multispectral (MS) CNC indices applicable across a wide range of crop conditions. We collected 449 canopy reflectance spectra (400–980 nm) together with corresponding CNC and AGB measurements across four growth stages of ryegrass (winter and summer), and five growth stages of barley (winter-spring) in Victoria, Australia, in 2018 and 2019. All possible waveband (400–980 nm) combinations revealed that the best combination varied between seasons and crop types. However, the visible spectrum, particularly the blue region, presented high and consistent performance. Bandwidths of 10–40 nm outperformed either very narrow (2 nm) or very broad bandwidths (80 nm). The newly developed 2-waveband index (416 and 442 nm with 10-nm bandwidth; R2 = 0.75 and NRMSE = 0.2) and 4-waveband index (512, 440, 414 and 588 nm with 40-nm bandwidth; R2 = 0.81 and NRMSE = 0.17) exhibited the best performance, while validation with an independent dataset (from a different growing period to those used in the model development) obtained NRMSE values of 0.25 and 0.24, respectively. The 4-waveband index provides enhanced performance and permits use of broader bandwidths than its 2-waveband counterpart.

Optimizing Spectral Waveband Selection for Spectral Radiation Detection of Hypersonic Vehicle

Space-based spectral radiation detection is one of the most effective approaches for detecting hypersonic vehicles. However, due to complex characteristics of the plasma sheath that is generated by aerodynamic thermal effect and envelopes hypersonic vehicle, space-based spectral radiation detection is still confronted with great challenges. Based on the mechanisms of spectral radiation and transmission, we establish a model to conduct optimal selection on spectral wavebands for detecting hypersonic vehicle. Through using the established model, we obtain the spectral radiation characteristics of plasma-sheath-enveloped vehicles (RAM-CII and HTV2) under different flight conditions (at 40–70-km altitude with 10–25-Mach velocity). The optimal spectral wavebands for space-based detection are analyzed and identified using the calculated spectral data, upon which the results reveal that significant differences exist in spectral radiation intensities of different flight status for those two vehicles. The overall radiation intensity of RAM-CII vehicle is higher than that of HTV2 vehicle, to which the optimized detection wavebands corresponding to velocity and altitude differ. Generally, at different altitudes, the optimal wavebands for both vehicles are 5.00–7.86, 2.59–3.44, and 1.81–2.10 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> . At different flight speeds, the optimal bands are 0.78–1.13 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> and 99–130 nm, respectively. Combined with the optimized speed and height bands, the optimal detection bands of RAM-CII vehicle and HTV2 vehicle are 5.00–7.86 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ \mu \text{m}$ </tex-math></inline-formula> , 2.59–3.44 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> , and 99–130 nm. The above findings can provide useful data for in-depth study of space-based spectral radiation detection.

Hyperspectral Image Data and Waveband Indexing Methods to Estimate Nutrient Concentration on Lettuce (Lactuca sativa L.) Cultivars.

Lettuce is an important vegetable in the human diet and is commonly consumed for salad. It is a source of vitamin A, which plays a vital role in human health. Improvements in lettuce production will be needed to ensure a stable and economically available supply in the future. The influence of nitrogen (N), phosphorus (P), and potassium (K) compounds on the growth dynamics of four hydroponically grown lettuce (Lactuca sativa L.) cultivars (Black Seeded Simpson, Parris Island, Rex RZ, and Tacitus) in tubs and in a nutrient film technique (NFT) system were studied. Hyperspectral images (HSI) were captured at plant harvest. Models developed from the HSI data were used to estimate nutrient levels of leaf tissues by employing principal component analysis (PCA), partial least squares regression (PLSR), multivariate regression, and variable importance projection (VIP) methods. The optimal wavebands were found in six regions, including 390.57-438.02, 497-550, 551-600, 681.34-774, 802-821, and 822-838 nm for tub-grown lettuces and four regions, namely 390.57-438.02, 497-550, 551-600, and 681.34-774 nm for NFT-system-grown lettuces. These fitted models' levels showed high accuracy (R2=0.85-0.99) in estimating the growth dynamics of the studied lettuce cultivars in terms of nutrient content. HSI data of the lettuce leaves and applied N solutions demonstrated a direct positive correlation with an accuracy of 0.82-0.99 for blue and green regions in 400-575 nm wavebands. The results proved that, in most of the tested multivariate regression models, HSI data of freshly cut leaves correlated well with laboratory-measured data.

Using Machine Learning for Nutrient Content Detection of Aquaponics-Grown Plants Based on Spectral Data

Nutrients derived from fish feed are insufficient for optimal plant growth in aquaponics; therefore, they need to be supplemented. Thus, estimating the amount of supplementation needed can be achieved by looking at the nutrient contents of the plant. This study aims to develop trustworthy machine learning models to estimate the nitrogen (N), phosphorus (P), and potassium (K) contents of aquaponically grown lettuce. A FieldSpec4, Pro FR portable spectroradiometer (ASD Inc., Analytical Spectral Devices Boulder, Boulder, CO, USA) was used to measure leaf reflectance spectra, and 128 lettuce seedlings given four NPK treatments were used for spectra acquisition and total NPK estimation. Principal component analysis (PCA), genetic algorithms (GA), and sequential forward selection (SFS) were applied to select the optimal wavebands. Partial least squares regression (PLSR), back-propagation neural network (BPNN), and random forest (RF) approaches were used to develop the predictive models of NPK contents using the selected optimal wavelengths. Good and significantly correlated predictive accuracy was obtained in comparison with the laboratory-measured freshly cut lettuce leaves with R2 ≥ 0.94. The proposed approach provides a pathway toward automatic nutrient estimation of aquaponically grown lettuce. Consequently, aquaponics will become more intelligent, and will be adopted as a precision agriculture technology.

Fast Detection of Diarrhetic Shellfish Poisoning Toxins in Mussels Using NIR Spectroscopy and Improved Twin Support Vector Machines

Diarrhetic shellfish poisoning (DSP) toxins are potent marine biotoxins. It can cause a severe gastrointestinal illness by the consumption of mussels contaminated by DSP toxins. New methods for effectively and rapidly detecting DSP toxins-contaminated mussels are required. In this study, we used near-infrared (NIR) reflection spectroscopy combined with pattern recognition methods to detect DSP toxins. In the range of 950-1700 nm, the spectral data of healthy mussels and DSP toxins-contaminated mussels were acquired. To select optimal waveband subsets, a waveband selection algorithm with a Gaussian membership function based on fuzzy rough set theory was applied. Considering that detecting DSP toxins-contaminated mussels from healthy mussels was an imbalanced classification problem, an improved approach of twin support vector machines (TWSVM) was explored, which is based on a centered kernel alignment. The influences of parameters of the waveband selection algorithm and regularization hyperparameters of the improved TWSVM (ITWSVM) on the performance of models were analyzed. Compared to conventional SVM, TWSVM, and other state-of-the-art algorithms (such as multi-layer perceptron, extreme gradient boosting and adaptive boosting), our proposed model exhibited better performance in detecting DSP toxins and was little affected by the imbalance ratio. For the proposed model, the F-measure reached 0.9886, and detection accuracy reached 98.83%. We explored the physical basis for the detection model by analyzing the relationship between the occurrence of overtone and combination bands and selected wavebands. This study supports NIR spectroscopy as an innovative, rapid, and convenient analytical method to detect DSP toxins in mussels.

A Sensitive Wavebands Identification System for Smart Farming

Sensing the content of macronutrients in the agricultural soil is an essential task in precision agriculture. It helps the farmers in the optimal use of fertilizers. It reduces the cost of food production and also the negative environmental impacts on atmosphere and water bodies due to indiscriminate dosage of fertilizers. The traditional chemical-based laboratory soil analysis methods do not serve the purpose as they are hardly suitable for site specific soil management. Moreover, the spectral range used in the chemical-based laboratory soil analysis may be of 350–2500 nm, which leads to redundancy and confusion. Developing sensors based on the discovery of spectral wavebands that respond to soil macronutrient concentrations, on the other hand, is an innovative and successful technology since the results are dependable and timely. The goal of this article is to use a supervised neuro-fuzzy based dimensionality reduction approach in the sensor development process to determine sensitive wavebands of soil macronutrients. Accordingly, the spectral signatures of the soil are collected in an outdoor environment and mapped with its macronutrient concentrations. In this spectral analysis, the spectral reflectance of 424 wavelengths has been obtained and these wavelengths are evaluated through combined and individual modes as well. Appropriate wavelengths are selected in each case by minimizing the fuzzy reflectance assessment index. The effectiveness of these selected wavelengths in each mode is validated by modeling the relation between the reduced reflectance space and each macronutrient concentration using Partial Least Squares Multi Variable Regression (PLS-MVR) method. Set of optimal wavebands are identified and the results are compared with the existing systems.

Identifying optimal waveband positions for discriminating Parthenium hysterophorus using hyperspectral data

Parthenium hysterophorus (an alien weed) is posing serious threat to crop yields, livestock's and human health and is considered the seventh most devastating weed across the globe. Early identification and mapping of Parthenium hysterophorus is essential for the timely eradication and control of this alien invasive species. Hyperspectral remote sensing (due to its high spectral details) is highly valuable in discriminating vegetation species and mapping its distribution. However, the use of high-dimensional hyperspectral data possesses the threat of multi-collinearity (i.e., contiguous wavelength-bands exhibits strong spectral correlation) which in-turn yields unstable parameter estimation. This study aims to explore the potential of hyperspectral (spectroscopic) data for discerning Parthenium hysterophorus and to identify optimal wavebands that are sensitive for species discrimination. In this study, the spectral signatures of Parthenium hysterophorus and four co-occurring plant species were acquired using portable hand held spectrometer. Spectral Angle Mapper (SAM) in conjunction with Genetic Algorithms (GA) were used discern the measured species based on their spectral profiles. The analysis yielded high classification accuracies for both the training (overall accuracy = 99%) and testing (overall accuracy = 97%) datasets. The SAM-GA picked a meaningful subset of spectral bands from different parts of electromagnetic spectrum (i.e., centering at 0.47 μm, 0.715 μm, 1.12–1.25 μm and 1.8–1.9 μm) which carries highest information for the spectral discrimination of Parthenium hysterophorus. In conclusion, this study confirms the capability of hyperspectral data in discerning Parthenium hysterophorus from other crops/plant species and also highlights the importance of few wavelength positions for the spectral discrimination of Parthenium hysterophorus weed.

Detection of foreign materials in cocoa beans by hyperspectral imaging technology

The presence of foreign materials in a batch of cocoa beans affect its profitability, marketability and overall quality grade of the product. Therefore, the identification of these materials and their subsequent removal is very important to ensure the high quality of the final product. This study aims to investigate the feasibility of using hyperspectral imaging technology for the detection and discrimination of four categories of foreign materials (wood, plastic, stone and plant organs) that are relevant to the cocoa processing industries. The spectral image data of 250 cocoa beans and foreign material was analyzed using principal component analysis and three classification models Support Vector Machine (SVM) Linear Discriminant Analyses (LDA) and K Nearest Neighbours (KNN). Optimal wavebands, which were obtained from the second spectra graph and the first three PCs, were fed into the classification models and the performance of classifiers was compared. The results showed that SVM could reach over 89.10% accuracy in classifying cocoa beans and foreign materials. The accuracy of the SVM classifier when using optimal features as input was 86.90% for the training set and 81.28% for the test set. An external test set of data was used to test the generalization of the model. The results showed that the classification of foreign materials could be more robust when the optimal feature was used as input data.

Application of spectral imaging for safety inspection of fresh cut vegetables

Food safety remains a critical issue and receiving increasing attention in both developed and developing countries. Fresh-cut vegetables may contain foreign objects such as small plastic pieces or biological contaminants (bugs and worms). In order to comply with requirements for product safety and maintain consumer confidence, there is a need for high-throughput, non-invasive technique for foreign objects detection in and real-time sorting of fresh-cuts. In this work, a liquid crystal tunable filter (LCTF) based spectral imaging system in the spectral range from 420 nm to 730 nm has developed and integrated with a conveyor unit. A software-interface was developed to collect the selected band images of moving samples and real time image processing for potential foreign materials detection on fresh cut vegetables. Spectral images throughout the whole spectral region were first collected for both fresh-cuts and foreign materials. The optimal wavebands for discriminating between foreign materials contaminated and sound fresh-cut were investigated. The optimal wavebands for each fresh-cut were selected and thus the developed system was used in multispectral imaging mode. Image processing algorithms were developed for detection of all potential foreign materials in fresh cut processing. The obtained results demonstrate that several kinds of foreign materials can be detected effectively despite being similar in color or nearly transparent. The ultimate advantage of a developed system is that it can be used either as in fluorescence mode or for collection of multispectral images in visible regions.

Sedimentary phosphate classification based on spectral analysis and machine learning

The process of phosphate extraction can significantly benefit from the advances in spectral analysis and Artificial Intelligence to reduce the cost of the drilling operation. The ambiguities caused by the apparent similarities between different layers and by the existing mineralogical alterations complexify the delineation of phosphate layers with conventional vision systems. In this paper, we established a spectral signature database of representative samples collected from the Ben Guerir deposit in Morocco, over the 250–2500 nm spectral range (mid-ultraviolet to mid-infrared). The aim is to build a spectral database of the samples and select an optimal waveband set capable of good discrimination, which can be used in a multispectral inspection system for an accurate delimitation of the soil layers during the drilling operation. First, the reflectance signatures of the extracted soil samples were collected. Second, principal component analysis (PCA) loadings were investigated to determine the most informative feature subspaces. A Bhattacharyya distance (B-distance) separability test was then implemented to select the most dissociating combinations of 3 bands from these subspaces. Finally, a machine learning classification test was used to evaluate the capacity of the selected features to discriminate phosphate samples. The results demonstrate the impact of selecting an informative reduced feature set and show good discrimination rates based on the combined information of wavelengths from the Ultraviolet (UV) or the Near-infrared (NIR) spectral ranges.

Detecting Biophysical Characteristics and Nitrogen Status of Finger Millet at Hyperspectral and Multispectral Resolutions

Finger millet (Eleusine coracana Gaertn L.) is an important grain crop for small farmers in many countries. Reliable estimates of crop parameters, such as crop growth and nitrogen (N) content, through remote sensing techniques can improve in-season management of finger millet. This study investigated the relationships of hyperspectral reflectance with canopy height, green canopy cover, leaf area index (LAI), and N concentrations of finger millet using an optimal waveband selection procedure with partial least square regression (PLSR). Predictive performance of 13 vegetation indices (VIs) computed from the original hyperspectral data as well as synthesized Landsat-8 and Sentinel-2 data were evaluated and compared for estimating various crop parameters with simple linear regression (SLR) and multilinear regression (MLR) models. The optimal wavebands determined by PLSR were mostly concentrated within 1,000–1,100 nm for both LAI and dry biomass but were scattered for other canopy parameters. The SLR statistics resulted in the simple ratio pigment index (SRPI) and red/green index (RGI) performing best when predicting LAI (R2v = 0.53–0.59) and canopy cover (R2v = 0.72–0.76). The blue/green index (BGI1) was strongly related to canopy height (R2v = 0.65–0.78), dry biomass (R2v = 0.42–0.49), and N concentration (R2v = 0.70–0.83) of finger millet, regardless of spectral resolutions. The MLR approach, using four maximum VIs as input variables, improved the prediction accuracy of N concentration by 14% compared to both SLR and waveband selection methods. VIs computed from synthesized Landsat-8 and Sentinel-2 satellite data resulted in similar or greater prediction accuracy than hyperspectral data for various canopy parameters of finger millet, indicating publicly accessible multispectral data could serve as alternative to hyperspectral data for improved crop management decisions via precision agriculture.

Hyperspectral imaging techniques for rapid detection of nutrient content of hydroponically grown lettuce cultivars

The analyses of online quality measurements of four lettuce cultivars (Rex, Tacitus, Black Seeded Simpson, Flandria) using hyperspectral image processing techniques have been studied. Seedlings were planted in Rockwool cubes and fed for 3 weeks using hydroponic nutrient solution containing 0, 50, 100, 150, 200, 250, and 300 ppm nitrogen. The hyperspectral images of the freshly cut lettuce leaves were captured using hyperspectral camera in the wavelength range of 400–1,000 nm. The algorithm to measure nutrient levels of leaf tissues including nitrate (NO3–), calcium (Ca2+), potassium (K+), soluble solid content (SSC), pH, and total chlorophyll concentration (SPAD reading) for lettuce was developed. To locate the optimum wave band combination values, simple linear regression models with two spectral data values, namely reflectance and its first derivative, and two spectral indices, namely ratio spectral index (RSI) and normalized difference spectral index (NDSI), were found. The optimal wave bands were within 506–601 nm and 634–701 nm. The nutrient levels of NO3–, Ca2+, K+, SSC, pH, and SPAD in the leaf samples were estimated using partial least squares regression (PLSR) and principal component analysis (PCA) techniques of the four optimal wave bands. Both PLSR and PCA models produced well correlated outcome in comparison with the laboratory measured freshly cut lettuce leaves in predicting nutrient contents with R2 = 0.784–0.987.

A cloud computing-based approach using the visible near-infrared spectrum to classify greenhouse tomato plants under water stress

The objective of this study was to evaluate the potential of cloud computing technology for classifying protected tomato plants under different watering treatments. Two tomato varieties, HeZuo 903 and WanShiRuYi, were used in protected cultivation for two seasons. Three water treatments were conducted (normal watering, no watering during the first fruit-swelling period, and no watering during the both fruit-swelling periods). The visible near-infrared reflection spectra of the tomato canopies were collected during the fruiting period. Three spectral datasets were used, including the original reflection spectra, the first derivative of the reflection spectra, and the absorbance spectra. The successive projections algorithm(SPA) was used to select data from six wavebands (483, 557, 674, 783, 869, and 964 nm) as optimal wavebands. The cloud computing platform was built using the Hadoop and Spark frameworks. The MLlib machine-learning library from the Spark framework was used to build a multilayer perceptron classifier (MLPC) and one-vs.-rest classifier (ORC). These multi-class classifiers were applied to the spectral datasets (original, first derivative, and absorbance) for the two tomato varieties under different water treatments. For each classifier, 70% of the data was randomly selected for training and the remaining 30% was used for prediction. Training and prediction were conducted on the cloud computing platform. The MLPC had better classification accuracy than the ORC. Among the three spectral datasets, the first derivative of the spectra had the best classification performance, while the reflection and absorbance spectra had similar performances. Using the full waveband spectrum provided higher classification accuracy than using only the optimal wavebands. Furthermore, the tomato canopy spectrum classification performance was better for the WanShiRuYi plants than the HeZuo 903 plants. Moreover, the collected spectral dataset was increased in size to evaluate the operating efficiency of the cloud computing platform when processing ‘big data.’ The operating efficiency was significantly improved by increasing the size of the spectral dataset or the number of nodes in the platform. Finally, the python and TensorFlow were used to implement the CNN algorithm, and conducted classification and analysis of the spectral datasets. The results showed that the MLPC and ORC algorithm had better classification performance than the CNN algorithm in classification of spectral data.

UAV-Based Hyperspectral Imaging Technique to Estimate Canola (Brassica napus L.) Seedpods Maturity

Identification of optimal pod maturity stage in Canola is key for maximizing seed yield, quality and also an important phenotypic trait in crop improvement programs. The conventional method is via visual inspection of seed color change. Alternatively, hyperspectral sensors have potential to determine physiological status of the crops. Therefore, the objective of the study is to estimate canola seed maturity using field-based hyperspectral imaging. For this study, five canola genotypes (NAM-0, NAM-13, NAM-17, NAM-48, and NAM-76) were selected from an experiment of 56 populations. The experimental field was imaged using a UAV-mounted hyperspectral camera (400–1,000 nm) at five growth stages starting from pod formation to near-harvest maturity (BBCH-79(S1) to 88(S5)). For each genotype; pod and seed moisture were estimated on the same day of imaging. First-order derivative was conducted on reflectance data to determine optimal spectral wavebands. As a part of this study, a new vegetation index denoted “Canola-Pod-Maturity Index (CPMI)” was developed. CPMI was evaluated in comparison with four existing vegetation indices (mNDRE, PSRI, MCARI, WBI). CPMI showed a stronger relationship ( 0.81–0.98 for pods and 0.66–0.85 for seeds) with pod and seed moisture for all the genotypes. Furthermore, the new index was able to find differences among genotypes with variable maturity times.

Identification of heat damage in imported soybeans based on hyperspectral imaging technology

Imported soybeans are prone to heat damage due to high storage temperatures or poor ventilation during transportation. Heat damage directly degrades the quality of the produce and greatly reduces the edible value of soybeans. Rapid and nondestructive techniques for assessing the quality of imported soybeans are in demand. Hyperspectral imaging (HSI) technology was used to distinguish sound soybeans from heat-damaged soybeans. For testing the effectiveness of preprocessing methods in enhancing model performance, five different preprocessing methods were implemented to original spectra. To solve problems related to accuracy, efficiency, and model interpretability caused by high-dimensional HSI data, three waveband selection algorithms - dependency measure (DM-NRS), mutual information (MI-NRS) and variable precision (VP-NRS) - based on neighborhood rough set (NRS) theory were proposed to identify the waveband subsets with optimal distinguishing ability. The effectiveness of preprocessing methods and waveband selection algorithms was validated by establishing two kinds of models: extreme learning machine (ELM) and random forest (RF) models. In addition to the classification accuracy, the robustness of the waveband selection algorithms was studied. The results demonstrated that the Savitzky-Golay (SG) smoothing preprocessing method combined with the MI-NRS waveband selection algorithm and the ELM classifier achieved the best classification and robustness results. Classification accuracy reached 99.98% when using only two optimal wavebands, and reached 100% when using more than four optimal wavebands. The results prove that the HSI technology is an accurate, effective, and nondestructive technique for classifying sound and heat-damaged soybeans. © 2019 Society of Chemical Industry.

Natural vegetation cover analysis in Wadi Hagul, Egypt using hyperspectral remote sensing approach

Natural vegetation of Wadi Hagul in the eastern desert of Egypt suffers from serious changes due to the uncontrolled human activities. Present research is the first attempt to study the natural vegetation cover of Wadi Hagul in regard of spectroscopic characteristics. This was achieved by analyzing the relationship between the spectral signatures of wild plants and variation of the morphological and ecological characteristics using hyperspectral sensor. ASD field Spec was used to measure the reflection of 27 plant species in different localities. The optimal waveband was firstly selected then the optimal wavelength in order to discriminate the studied taxa using ANOVA-Tukey’s and LDA analysis. The vitality of the plant was estimated using numbers of vegetation indices derived from spectroscopic measurements. Multi-temporal Landsat images were processed to produce NDVI to detect the spatiotemporal change in vegetation cover. The study analyses showed that the investigated plant species of Wadi Hagul are belonging to 19 genera and 12 families. Statistical analyses indicated that Red and NIR spectral zones are the optimal zones to differentiate various plant species. The optimal wavelength of each plant species was found variable and analyses showed that there are wide spectral unique zones for Centaurea aegyptiaca and Ochradenus baccatus, which facilitate separating these species by various sensors. Indices analyses showed that most of the investigated plant species lies in the good conditions. It can be concluded the spectral signature of the wild plant species is very necessary for documentation of the natural vegetation in such Wadies.

Application of thermal imaging and hyperspectral remote sensing for crop water deficit stress monitoring

Water deficit in crops induces a stress that may ultimately result in low production. Identification of response of genotypes towards water deficit stress is very crucial for plant phenotyping. The study was carried out with the objective to identify the response of different rice genotypes to water deficit stress. Ten rice genotypes were grown each under water deficit stress and well watered or nonstress conditions. Thermal images coupled with visible images were recorded to quantify the stress and response of genotypes towards stress, and relative water content (RWC) synchronized with image acquisition was also measured in the lab for rice leaves. Synced with thermal imaging, Canopy reflectance spectra from same genotype fields were also recorded. For quantification of water deficit stress, Crop Water Stress Index (CWSI) was computed and its mode values were extracted from processed thermal imageries. It was ascertained from observations that APO and Pusa Sugandha-5 genotypes exhibited the highest resistance to the water deficit stress or drought whereas CR-143, MTU-1010, and Pusa Basmati-1 genotypes ascertained the highest sensitiveness to the drought. The study reveals that there is an effectual relationship (R 2 = 0.63) between RWC and CWSI. The relationship between canopy reflectance spectra and CWSI was also established through partial least square regression technique. A very efficient relationship (calibration R 2 = 0.94 and cross-validation R 2 = 0.71) was ascertained and 10 most optimal wavebands related to water deficit stress were evoked from hyperspectral data resampled at 5 nm wavelength gap. The identified ten most optimum wavebands can contribute in the quick detection of water deficit stress in crops. This study positively contributes towards the identification of drought tolerant and drought resistant genotypes of rice and may provide valuable input for the development of drought-tolerant rice genotypes in future.

LW-NIR hyperspectral imaging for rapid prediction of TVC in chicken flesh

Total viable count (TVC) is often used as an important indicator for chicken freshness evaluation. In this study, 112 fresh chicken flesh samples were acquired after slaughtered and their hyperspectral images were collected in the LW-NIR (900-1700 nm) range. The full LW-NIR spectra (486 wavebands) within the images were extracted and applied to related to reference TVC values measured in different storage period, using partial least squares regression (PLSR) algorithm, resulting in high correlation coefficients (R) and low root mean square errors (RMSE), for either raw spectra or pretreatment spectra. By using regression coefficients (RC) method, 20, 18, 17 and 20 optimal wavebands were respectively selected from raw spectra, baseline correction (BC) spectra, Savitzky-Golay convolution smoothing (SGCS) spectra and standard normal variate (SNV) spectra and applied for the optimization of original full waveband PLSR model. By comparison, RC-PLSR model based on the SGCS spectra showed a better performance in TVC prediction with RC of 0.98 and RMSEC of 0.35 log10 CFU/g in calibration set, and RP of 0.98 and RMSEP of 0.44 log10 CFU/g in prediction set. At last, by transferring the best RC-PLSR model, the dynamic TVC change during the storage was visualized by color maps to indicate the TVC spoilage degree. The overall study revealed that LW-NIR hyperspectral imaging combined with PLSR could be used to predict the freshness of chicken flesh. Keywords: hyperspectral imaging, chicken, TVC, partial least square regression (PLSR) DOI: 10.25165/j.ijabe.20191203.4444 Citation: H Wang, H J He, H J Ma, F S Chen, Z L Kang, M M Zhu, et al. LW-NIR hyperspectral imaging for rapid prediction of TVC in chicken flesh. Int J Agric & Biol Eng, 2019; 12(3): 180–186.