Nondestructive measurement of internal quality of Nanfeng mandarin fruit by charge coupled device near infrared spectroscopy

The soluble solids content (SSC) and total acidity (TA) are the major characteristics for assessing quality and maturity of Nanfeng mandarin fruits. The feasibility of charge coupled device near infrared spectroscopy (CCD-NIRS) combining with effective wavelengths selection algorithm used to measure SSC and TA nondestructively was investigated. The effective wavelengths to SSC and TA were chosen by interval partial least squares (iPLS) in the wavelength range of 600–980 nm. The predictive ability of SSC model used PLS regression was improved with r of 0.92 and RMSEP of 0.65 °Brix using effective wavelengths of 681.36–740.51 nm, 798.60–836.19 nm and 945.52–962.75 nm. The TA model was simplified with r of 0.64 and RMSEP of 0.09% using effective wavelengths of 817.57–836.19 nm, 909.85–927.60 nm and 945.52–962.75 nm. The experimental results demonstrated that the CCD-NIRS technique combining with iPLS algorithm was a feasible method to measure SSC and TA of Nanfeng mandarin fruits nondestructively.     

Hyperspectral detection of rice damaged by rice leaf folder (Cnaphalocrocis medinalis)

The reflectance from rice (Oryza sativa L.) leaves and canopy damaged by rice leaf folder (RLF), Cnaphalocrocis medinalis (Guenée) was studied at the booting stage in order to establish a monitoring method for RLF based on hyperspectral data. The results showed that reflectance from rice leaves significantly decreased in the green (530–570 nm) and near infrared (700–1000 nm) regions, and significantly increased in the blue (450–520 nm) and red (580–700 nm) regions as the leaf-roll rate of rice increased. Reflectance from rice canopy significantly decreased in the spectral regions from 737 to 1000 nm as the infestation scale of RLF increased, and the most correlation appeared at 938 nm. Seven spectral regions 503–521, 526–545, 550–568, 581–606, 688–699, 703–715, and 722–770 nm at leaf-level, and one region 747–754 nm at canopy-level were found to be sensitive bands to exhibit the damage severity in rice by RLF. The position of the red edge peak remarkably moved to blue region, and the amplitude and area of the red edge significantly decreased when rice leaves were severely infected by RLF. Thirty-eight spectral indices at leaf-level and 29 indices at canopy-level were found to be sensitive to leaf-roll rate and infestation scale in rice, respectively. The linear regression models were built to detect the leaf-roll rate (0.0–1.0) and infestation scale (0–5) in rice using leaf- and canopy-level reflectance data. The root mean square error of the model was only 0.059 and 0.22 for the leaf-roll rate and infestation scale, respectively. These results suggested that the hyperspectral reflectance was potential to detect RLF damage severity in rice.     

Development of software for spectral imaging data acquisition using LabVIEW

Developing data acquisition software is a major challenge in integrating a spectral imaging system. This paper presents the design and implementation of a data acquisition program using LabVIEW for a liquid crystal tunable filter based spectral imaging system (900–1700 nm). The module-based program was designed in a three-tier structure. The image acquisition process, modelled by a finite state machine, was implemented in LabVIEW to control the spectral imaging system to collect hyperspectral or multispectral images. The collected spectral images were encoded in general format and could be further processed by other common spectral image analysis tools. In addition, the program could be used to observe band ratio images of the test object in real-time, collect spectral images after ensemble averaging, and select region of interest for spectral image acquisitions. This program is a useful data acquisition tool for the filter-based spectral imaging system. The design and implementation techniques described in this article could also be used to develop similar spectral image acquisition programs.     

Comparison of segmentation algorithms for cow contour extraction from natural barn background in side view images

Computer vision techniques are a means to extract individual animal information such as weight, activity and calving time in intensive farming. Automatic detection requires adequate image pre-processing such as segmentation to precisely distinguish the animal from its background. For some analyses such as gait analysis, a side view perspective is recommended. When using a side view angle however, the background is more difficult to control – moving objects, such as other animals may negatively impact successful image segmentation. The objective of this research was to evaluate five different background segmentation algorithms on side view images when taken against a static background (a solid transportable wall) and a dynamic background (open air, without a wall).The experiments were conducted on a commercial robotic-milking dairy farm in Israel with a herd size of 70 Israeli Holstein cows. A side view image of cow’s gait was recorded after milking when the cows exited the milking area and returned to the cowshed. From the recording database, a random selection was made of 35 frames containing a static background (solid wall) and 20 frames containing a dynamic background (natural barn environment with other cows).Five segmentation algorithms were chosen and adapted from literature to extract the cow shape from the image. The output of three algorithms gave the cow’s full body shape two identified only the contour of the cow’s body. The algorithms were compared on their ability to correctly identify the cow’s back contour line. The performance of each algorithm was quantified by comparing its outputs to a golden standard of manually labelled cow pixels in the image.The introduction of a physical wall behind the cows (static background) significantly improved the foreground segmentation results (Mean Absolute Error (MAE) = 6.7 ± 5.7 pixels vs. 19.7 ± 9.1 pixels). The fourth algorithm, based on an edge detection on the background difference frame, gave the best cow back contour line segmentation results (b₀ = −0.4 ± 15.5 and b₁ = 1.00 ± 0.07). The fifth algorithm which is based on consecutive frame differences was less accurate than the other four methods which are based on the background frame differences (MAE = 16.0 ± 5.9 pixels vs. 4.1 ± 2.2 pixels, 4.3 ± 2.2 pixels, 5.6 ± 2.8 pixels and 3.7 ± 1.4 pixels respectively for the other four algorithms). The results show that the applied algorithms were not robust enough to work on side view images with dynamic backgrounds.     

A liquid crystal tunable filter based shortwave infrared spectral imaging system: Design and integration

This paper presents the methodology to design and integrate a liquid crystal tunable filter (LCTF) based shortwave infrared (SWIR) spectral imaging system. The system consisted of an LCTF-based SWIR spectral imager, an illumination unit, a frame grabber, and a computer with the data acquisition software. The spectral imager included an InGaAs camera (320 × 256 pixels), an SWIR lens (50 mm, F/1.4), and an LCTF (20 mm aperture). Four multifaceted reflector halogen lamps (35 W, 12 VDC) were used to build the illumination unit. The system was integrated by a LabVIEW program for data acquisition. It can capture hyperspectral or multispectral images of the test object in the spectral range of 900–1700 nm. The system was validated by differentiating sugar from wheat flour, and water from 95% ethanol. The results showed that the system can distinguish these materials in both spectral and spatial domains. This SWIR spectral imaging system could be a potential useful tool for nondestructive inspection of food quality and safety.     

Application of Response Surface Methodology to systematically optimize image quality in computer tomography: A case study using fresh chestnuts (Castanea spp.)

The objective of this study is to describe a method that will systematically and efficiently obtain a model of computerized tomographic (CT) scanning factor levels, which returns optimized high quality CT images. Chestnut (Castanea spp.) two-dimensional CT images were used to describe this optimization procedure, considered to be a critical step in the development of a fast, nondestructive technique, capable of assessing fresh internal quality attributes and components of chestnuts, and other agricultural commodities. Response Surface Methodology (RSM), using a three-factor, three-level Box–Behnken statistical design and digital image processing, were used to optimize the factors affecting image quality, which include X-ray voltage, current, and slice thickness. Response variables representing image quality were digitally and automatically inferred from fresh chestnut image Signal to Noise Ratio, Teflon® cylinder reference volume accuracy, quality assurance (QA) High Contrast Spatial Resolution phantom, and QA Low Contrast Detectability phantom. Second-order RSM prediction models for each response variable reflected a combined maximized CT image quality at a voltage, current, and slice thickness equal to 120 kV, 170 mA, and 2.5 mm respectively. The experiment yielded optimal chestnut CT images that can accurately reflect internal decay of fresh chestnuts with an overall accuracy rate equal to 96%, taking as reference data the of Subjective Quality Rating of five trained chestnut experts.     

A high quality low-cost digital microscope minirhizotron system

An easily reproducible system is presented for minirhizotron image acquisition based on a digital microscope and entirely built with low-cost components. The system weighs 2.28 kg, is connected to a portable computer through USB, and allows collection and storage of high-quality digital images without other components or power sources. Settings at 25× magnification provide pixel sizes of 25 and 12 μm respectively with a medium pixel density microscope (640 × 480 pixel), and a high pixel density microscope (1280 × 1024 pixel). This kind of system coupled with recent developments of robotics and automatic image analysis for minirhizotron frames allows to envisage fast, low-labour and low-cost root investigation methods with a high degree of automation.     

A longitudinal study of the effect of time on the matching performance of a retinal recognition system for lambs

While there is strong evidence supporting retinal vascular pattern as a distinctive marker for sheep, it would be advantageous to get an insight into its robustness; in other words, to determine whether retinal recognition of young animals (lambs) can reach as good a matching performance as the one demonstrated for adult sheep. To this aim, a longitudinal study was devised to observe the evolution of matching scores (ms) of lamb retinal images (n = 38) acquired from 1 to 22 weeks after birth. It was observed that four lamb retinas (out of 38) underwent slight curving of one or two secondary arteries, which ceased by the time they were 6–8 weeks old. However, this slight artery curving did not affect matching performance. A random effects statistical model demonstrated that lamb age had an effect (P < 0.01) on the matching scores produced using this commercially available retinal recognition system. As lambs grew older (larger eyes) and they became easier to handle, retinal images of progressively better quality could be obtained in a more consistent way; and thus matching scores increased from an average of 86 at the first week of life, up to an average of 96 by week 8. Dunnett simultaneous tests of means indicated that no further improvement in matching score took place once lambs were at least 6–8 weeks old, meaning that the retinal image quality became by then optimal and consistent. Although the variable retinal image quality of younger lambs (1–4 weeks old) caused a reduction in matching score, they did not lead to false non-matches in any case (considering a cut-off matching score of 70 for acceptance of a positive match). Therefore, the results of these trials have shown that, with the available technology, retinal images can be used as a robust biometric marker of lambs from 1 week of age.     

Using vegetation index and modified derivative for early detection of soybean plant injury from glyphosate

Glyphosate is a non-selective, systemic herbicide highly toxic to sensitive plant species. Its use has seen a significant increase due to the increased adoption of genetically modified glyphosate-resistant crops since the mid-1990s. Glyphosate application for weed control in glyphosate-resistant crops can drift onto an off-target area, causing unwanted injury to non-glyphosate resistant plants. Thus, early detection of crop injury from off-target drift of herbicide is critical in crop production. In non-glyphosate-resistant plants, glyphosate causes a reduction in chlorophyll content and metabolic disturbances. These subtle changes may be detectable by plant reflectance, which suggests the possibility of using optical remote sensing for early detection of drift damage to plants. In order to determine the feasibility of using optical remote sensing, a greenhouse study was initiated to measure the canopy reflectance of soybean plants using a portable hyperspectral image sensor. Non-glyphosate resistant soybean (Glycine max L. Merr.) plants were treated with glyphosate using a pneumatic track sprayer in a spray chamber. The three treatment groups were control (0 kg ae/ha), low dosage (0.086 kg ae/ha), and high dosage (0.86 kg ae/ha), each with four 2-plant pots. Hyperspectral images were taken at 4, 24, 48, and 72 h after application. The extracted canopy reflectance data was analyzed with vegetation indices. The results indicated that a number of vegetation indices could identify crop injury at 24 h after application, at which time visual inspection could not distinguish between glyphosate injured and non-treated plants. To improve the results a modified method of spectral derivative analysis was proposed and applied to find that the method produced better results than the vegetation indices. Four selected first derivatives at wavelength 519, 670, 685, and 697 nm could potentially differentiate crop injury at 4 h after treatment. The overall false positive rate was lower than the vegetation indices. Furthermore, the derivatives demonstrated the ability to separate treatment groups with different dosages. The study showed that hyperspectral imaging of plant canopy reflectance could be a useful tool for early detection of soybean crop injury from glyphosate, and that the modified spectral derivative analysis had a better performance than vegetation indices.     

Analysis of hyperspectral scattering images using locally linear embedding algorithm for apple mealiness classification

Hyperspectral scattering images between 600 nm and 1000 nm were acquired for 580 ‘Delicious’ apples for mealiness classification. A locally linear embedding (LLE) algorithm was developed to extract features directly from the hyperspectral scattering image data. Partial least squares discriminant analysis (PLSDA) and support vector machine (SVM) were applied to develop classification models based on the LLE, mean-LLE and mean spectra algorithms. The model based on the LLE algorithm achieved an overall classification accuracy of 80.4%, compared with 76.2% by the mean-LLE algorithm and 73.0% by the mean spectra method for two-class classification (i.e., mealy and nonmealy) coupled with PLSDA. For the SVM models, the LLE algorithm had an overall classification accuracy of 82.5%, compared with 79.4% by the mean-LLE algorithm and 78.3% by the mean spectra method. Hence, the LLE algorithm provided an effective means to extract hyperspectral scattering features for mealiness classification.     

Bruise detection on ‘Golden Delicious’ apples by vis/NIR spectroscopy

Visible and near-infrared reflectance spectra were used to distinguish the bruises from the intact surfaces on ‘Golden Delicious’ apples. Reflectance spectra of apples were acquired for both bruised and intact surfaces within the range of 400–1700 nm. The effective wavebands for detecting bruises were determined by analysis of the correlelogram. The wavebands around 545 and 1200 nm clearly show the time evolution of the bruised tissue. The mean-normalized reflectance difference between wavebands centred at 745 and 905 nm was also an effective discriminator for detecting old bruises. Afterwards, a quadratic discrimination analysis was performed based on the selected discriminators. The total classification error for the 1-day-old bruises was about 16.3%. A more extensive study was carried out to determine the effects of storage time on classification performance. The detection error was decreased with the elapse of storage time after bruising. However, it was difficult to remove the time influence on the classification accuracy if only depending on the intensity value of reflectance.     

Modeling of sows diurnal activity pattern and detection of parturition using acceleration measurements

This article suggests and assesses two different monitoring methods for detecting sows parturition using series of three-dimensions acceleration measurements previously classified into activity types. Two groups of sows are monitored: a first group (n = 9) provided with straw (S), and a second group (n = 10) where no straw is provided (NS); two types of activity are taken into account: high active behaviour (corresponding to feeding, rooting and nest building behaviours) and total active behaviour (including any active activity type). The first method suggests modeling sows’ diurnal pattern of activity using a saw-tooth function for the probability of being active and monitoring the series using a Dynamic Generalized Linear Model (DGLM). The second method is based on a cumulative sum of hourly differences of activity, from day-to-day. Both methods use a threshold value, optimized for each group, to detect the onset of farrowing. Best results in terms of sensitivity and specificity are observed for the cumulative sum method, using individual variance and monitoring high active (sensitivity = 100%; specificity = 100%) and total active behaviours (sensitivity = 100%; specificity = 95%). Results of the DGLM method indicate a sensitivity of 100% and a specificity of 89% in average for both group S and NS. Observing the occurrence of alarm times, the DGLM method allows (i) earlier detection of farrowing: 15 h before the onset of farrowing, for both groups, as compared to 9–12 for the other methods; and (ii) a better distribution of alarms, i.e. minimize the number of alarms occurring within the last 6 h before farrowing.     

Development approach and evaluation of the Nutrient Expert software for nutrient management in cereal crops

Meeting the demand for more food in the next 20–30 years requires intensifying cereal cropping systems and increasing current yields to about 70–80% of the genetic yield potential. A dynamic and robust nutrient management approach such as site-specific nutrient management (SSNM) will be essential to increase yields and optimize profits while maintaining the productivity of these intensive cropping systems. SSNM has increased yield and profit in rice, maize, and wheat in major cropping systems in Asia; but, crop advisors have found it complex and difficult to implement in the field. Nutrient Expert (NE) was developed to provide crop advisors with a simpler and faster way to use SSNM. NE enables crop advisors to develop SSNM recommendations using existing site information. Nutrient Expert for Hybrid Maize (NEHM) increased yield and profit of farmers in Indonesia and the Philippines. In Indonesia, NEHM increased yield by 0.9 t ha⁻¹, which increased profit by US$ 270 ha⁻¹ over farmer’s fertilizer practice (FFP). Compared with FFP, NEHM recommendations reduced fertilizer P (−4 kg ha⁻¹), increased fertilizer K (+11 kg ha⁻¹), and did not significantly change fertilizer N. In the Philippines, NEHM increased yield by 1.6 t ha⁻¹ and profit by US$ 379 ha⁻¹ compared with FFP. Compared with FFP, NEHM gave higher rates of all three nutrients (+25 kg N ha⁻¹, +4 kg P ha⁻¹, and +11 kg K ha⁻¹), which substantially increased fertilizer costs (US$ 64 ha⁻¹) but still increased profit by about six times the additional investment in fertilizer. NE accounts for the important factors affecting site-specific recommendations, which makes it a suitable starting point for developing nutrient management tools to reach more users.     

Model fusion for prediction of apple firmness using hyperspectral scattering image

Hyperspectral scattering image is an advanced technology widely used in non-destructive measurement of fruit quality. To develop a better prediction model for apple firmness, the present study investigates a model fusion method coupled with wavelength selection algorithms. The current paper first discusses two wavelength selection algorithms, namely, uninformative variable elimination (UVE) and supervised affinity propagation (SAP). The selected effective wavelengths are then set as input to the partial least square (PLS) model. Six hundred “Golden Delicious” apples were analyzed. The first 450 apples were used as sample for the calibration model, whereas the remaining 150 were used for the prediction model. Compared with full wavelengths, the number of effective wavelengths based on the UVE and SAP algorithms decreased to 34% and 35%, but the correlation coefficient of prediction (Rp) increased from 0.791 to 0.805 and 0.814, whereas the root mean-square error of prediction (RMSEP) decreased from 6.00 to 5.73 and 5.71 N, respectively. A fusion model was then developed using UVE-PLS and SAP-PLS models coupled with backpropagation neural network. A better prediction accuracy was achieved from the fusion model (Rp = 0.828 and RMSEP = 5.53 N). The model fusion provides an effective modeling method for apple firmness prediction using hyperspectral scattering image technique.     

A feasibility study of the classification of Alpaca (Lama pacos) wool samples from different ages,sex and color by means of visible and near infrared reflectance spectroscopy

The usefulness of classifying the Alpaca wool samples according to their color, sex and location is associated with their economic value in the market, hence adequate methods for rapid classification are needed to assess the of wool value. This study evaluated the potential of the visible and near infrared (vis–NIR) spectroscopy combined with multivariate statistical analysis to classify Alpaca (Lama Pacos) fiber samples according to age (1 and 2–3-year-old), sex (Male and Female) and color (Black, Brown, LF and White). Samples (n = 291) were scanned in reflectance mode in the wavelength range of 400–2500 nm using a monochromator instrument (FOSS NIRSystems6500, Inc., Silver Spring, MD, USA). Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to classify fiber samples. Cross-validation was used for validation of classification models developed. Results showed that PLS-DA correctly classified 100% of fiber samples into ages, intermediate classification rates were obtained for color, while lower classification rates were obtained for the discrimination of wool samples according to sex. The results from this study suggested that vis–NIR spectroscopy in combination with multivariate data analysis can be used as a rapid method to classify Alpaca fiber samples according to age, sex and color.     

Automated system developed to control pH and concentration of nutrient solution evaluated in hydroponic lettuce production

Lettuce is one of the most widely consumed leaf vegetables. In hydroponic the growth depends upon the composition of nutrient solution. Due to its nutrient absorption, the conductivity and pH suffer continuous variations. This paper describes the development of a system completely managed by a lab-made software. It monitors the conductivity and pH throughout 24 h during the whole cycle of production. Also, allows adjust automatically any variation, through solenoid valves which dispense solutions of acid/base or nutrient. The efficiency of the proposed instrumentation was evaluated by simultaneously cultivation of same kind of lettuce (Vanda) in two different ways, hydroponics in greenhouse controlled with the developed devices, and grown conventionally in soil, adopted as referential. Agronomic and chemical parameters of commercial interest were analyzed for both crop, attesting the precocity in harvest (64 against 71 days) with reduced labor, better control and higher productivity, especially in fresh and dry matter of aerial parts, presenting 267.56 and 13.33 g plant⁻¹ respectively, using the developed system. The data sequence regarding the concentration of nutrients for the automated hydroponic system was similar to those obtained by the mentioned researchers, as follows: K > N > Ca > P > Mg > S > Fe > Zn > Mn > Cu. This similarity highlights the efficiency of controlling the parameters of conductivity and pH in the instrumental system applied to hydroponics, offering the producer an effective and viable alternative in the production of lettuce.     

Shadow effect on multi-spectral image for detection of nitrogen deficiency in corn

The efficiency of side-dressing, a more efficient of nitrogen application method than uniform application in either late Fall or early Spring, relies heavily on the capability of nitrogen deficiency detection on a sprayer. To determine the site-specific yield potential for corn, multi-spectral image analysis including aerial- and ground-based images has been used. Some acceptable calibration relationships between the multi-spectral reflectance and SPAD readings have been found from previous study. In sunny weather conditions there was a shadow in the image made by corn leaf itself. This research investigated the shadow effect on the image for detecting corn nitrogen deficiency based on corn canopy reflectance information. The results indicated that the reflectance of red channel in shadow area showed strong inverse correlation, so the vegetation index NDVI using red and NIR channels also showed strong correlation (R² = 77) compared to the whole leaf and bright area. And the reflectance (green and red) and vegetation index(G_NDVI, NDVI, and ratio) in shadow area showed more consistent correlations than others using these image analysis methods.     

Identification of grapevine varieties using leaf spectroscopy and partial least squares

Grapevine variety identification is a matter of great interest in viticulture, which is currently addressed by visual ampelometry or wet chemistry genetic analysis. This paper reports the development of a simple and automatic method of classification of grapevine varieties from leaf spectroscopy. The method consists of a classifier based on partial least squares that discriminates among grapevine varieties using a hyperspectral image of a leaf measured in reflectance mode. Hyperspectral imaging was conducted with a camera with 1040 wavelength bands operating between 380 nm and 1028 nm. The classifier was created using 300 leaves, 100 of each of the varieties Vitis vinifera L., Tempranillo, Grenache and Cabernet Sauvignon. Monte-Carlo cross-validation confirmed the classifier’s performance for the three varieties, which exceeded 92% in all cases. The proposed method has proven to satisfactory classify among grape varieties, but certainly a wider range of grapevine cultivars should be tested before it gets implemented for local sensing with the aim of providing the wine industry with a fast, automatic, environmentally friendly and accurate tool for grapevine variety identification.     

Constrained predictive control of a greenhouse

A scheme for temperature control of a greenhouse is presented. The present work proposes an approach based on a combination of two different control schemes: Feedback Linearization (FL) and standard linear Model Predictive Control (MPC), using their advantages. The treated greenhouse is considered a non-linear Single-Input–Single-Output process and subject to strong external disturbances. Since the methodology used for solving the MPC + FL approaches generally leads to an optimization problem subject to state-dependent non-linear constraints, an alternative for its implementation is discussed. Two control techniques are compared, namely MPC + FL and Non-linear Model Predictive Control (NLMPC).