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- ItemApplication of Absorption and Scattering Properties Obtained through Image Pre-Classification Method Using a Laser Backscattering Imaging System to Detect Kiwifruit Chilling Injury(Basel : MDPI AG, 2021) Yang, Zhuo; Li, Mo; East, Andrew R.; Zude-Sasse, ManuelaKiwifruit chilling injury (CI) damage occurs after long-term exposure to low temperature. A non-destructive approach to detect CI injury was tested in the present study, using a laser backscattering image (LBI) technique calibrated with 56 liquid phantoms for providing absorption coefficient (µa) and reduced scattering coefficient (µs’). Calibration of LBI resulted in a true-positive (TP) classification of 91.5% and 65.6% of predicted µs’ and µa, respectively. The optical properties of ‘SunGold™’and ‘Hayward’ kiwifruit were analysed at 520 nm with a two-step protocol capturing pre-classification according to the LBI parameters used in the calibration and estimation with the Farrell equation. Severely injured kiwifruit showed white corky tissue and water soaking, reduced soluble solids content and firmness measured destructively. Non-destructive classification results for ‘SunGold™’ showed a high percentage of TP for severe CI of 92% and 75% using LBI parameters directly and predicted µa and µs’ after pre-classification, respectively. The classification accuracy for severe CI ‘Hayward’ kiwifruit with LBI parameter was low (58%) and with µa and µs’ decreased further (35%), which was assumed to be due to interference caused by the long trichomes on the fruit surface.
- ItemMethane prediction based on individual or groups of milk fatty acids for dairy cows fed rations with or without linseed(New York, NY [u.a.] : Elsevier, 2019) Engelke, Stefanie W.; Daş, Gürbüz; Derno, Michael; Tuchscherer, Armin; Wimmers, Klaus; Rychlik, Michael; Kienberger, Hermine; Berg, Werner; Kuhla, Björn; Metges, Cornelia C.Milk fatty acids (MFA) are a proxy for the prediction of CH4 emission from cows, and prediction differs with diet. Our objectives were (1) to compare the effect of diets on the relation between MFA profile and measured CH4 production, (2) to predict CH4 production based on 6 data sets differing in the number and type of MFA, and (3) to test whether additional inclusion of energy-corrected milk (ECM) yield or dry matter intake (DMI) as explanatory variables improves predictions. Twenty dairy cows were used. Four diets were used based on corn silage (CS) or grass silage (GS) without (L0) or with linseed (LS) supplementation. Ten cows were fed CS-L0 and CS-LS and the other 10 cows were fed GS-L0 and GS-LS in random order. In feeding wk 5 of each diet, CH4 production (L/d) was measured in respiration chambers for 48 h and milk was analyzed for MFA concentrations by gas chromatography. Specific CH4 prediction equations were obtained for L0-, LS-, GS-, and CS-based diets and for all 4 diets collectively and validated by an internal cross-validation. Models were developed containing either 43 identified MFA or a reduced set of 7 groups of biochemically related MFA plus C16:0 and C18:0. The CS and LS diets reduced CH4 production compared with GS and L0 diets, respectively. Methane yield (L/kg of DMI) reduction by LS was higher with CS than GS diets. The concentrations of C18:1 trans and n-3 MFA differed among GS and CS diets. The LS diets resulted in a higher proportion of unsaturated MFA at the expense of saturated MFA. When using the data set of 43 individual MFA to predict CH4 production (L/d), the cross-validation coefficient of determination (R2 CV) ranged from 0.47 to 0.92. When using groups of MFA variables, the R2 CV ranged from 0.31 to 0.84. The fit parameters of the latter models were improved by inclusion of ECM or DMI, but not when added to the data set of 43 MFA for all diets pooled. Models based on GS diets always had a lower prediction potential (R2 CV = 0.31 to 0.71) compared with data from CS diets (R2 CV = 0.56 to 0.92). Models based on LS diets produced lower prediction with data sets with reduced MFA variables (R2 CV = 0.62 to 0.68) compared with L0 diets (R2 CV = 0.67 to 0.80). The MFA C18:1 cis-9 and C24:0 and the monounsaturated FA occurred most often in models. In conclusion, models with a reduced number of MFA variables and ECM or DMI are suitable for CH4 prediction, and CH4 prediction equations based on diets containing linseed resulted in lower prediction accuracy. © 2019 American Dairy Science Association
- ItemInfluence of Processing Parameters on Fibre Properties during Twin-Screw Extrusion of Poplar Wood Chips(Basel : MDPI, 2022) Dittrich, Christian; Pecenka, Ralf; Selge, Benjamin; Ammon, Christian; Kruggel-Emden, HaraldFor sustainable agriculture, the contentious input of peat in growing media needs to be replaced by a substitute with the best possible water-holding capacity (WHC). Wood from fast growing poplar trees, cultivated in short rotation coppices (SRC), is a suitable alternative if it is processed correctly in a twin-screw extruder. The processing parameters, such as the aperture setting of the extruder, moisture content, and specific energy demand (SED), during twin-screw extrusion, as well as their influence on fibre properties such as WHC and particle size distribution, are investigated. SRC-poplar wood chips from clone Max3 are the raw material used for this research. As a result, the best volume-based WHC (75%) at −1 kPa suction tension was achieved for dry extruded wood chip fibre at an aperture setting of 15 mm and an SED of 340 kWh*t−1. The smallest SED of 140 kWh*t−1 was measured at apertures of 35 mm and 40 mm, which resulted in a volume-based WHC of approximately 30% and a dry matter mass flow during processing of 0.289 t*h−1 (40 mm). The particle size distribution of semi-dry wood chips has the highest fine fraction as well as the smallest coarse fraction. Conclusively, poplar wood can be processed fresh and dry into fibre at an acceptable SED, which results in an acceptable WHC.
- ItemMethods for Recognition of Colorado Beetle (Leptinotarsa decemlineata (Say)) with Multispectral and Color Camera-sensors(Berlin ; Heidelberg : Springer, 2022) Dammer, Karl-HeinzAt the beginning of an epidemic, the Colorado beetle occur sparsely on few potato plants in the field. A target-orientated crop protection applies insecticides only on infested plants. For this, a complete monitoring of the whole field is required, which can be done by camera-sensors attached to tractors or unmanned aerial vehicles (UAVs). The gathered images have to be analyzed using appropriate classification methods preferably in real-time to recognize the different stages of the beetle in high precision. In the paper, the methodology of the application of one multispectral and three commercially available color cameras (RGB) and the results from field tests for recognizing the development stages of the beetle along the vegetation period of the potato crop are presented. Compared to multispectral cameras color cameras are low-cost. The use of artificial neural network for classification of the larvae within the RGB-images are discussed. At the bottom side of the potato leaves the eggs are deposited. Sensor based monitoring from above the crop canopy cannot detect the eggs and the hatching first instar. The ATB developed a camera equipped vertical sensor for scanning the bottom of the leaves. This provide a time advantage for the spray decision of the farmer (e.g. planning of the machine employment, purchase of insecticides). In this paper, example images and a possible future use of the presented monitoring methods above and below the crop surface are presented and discussed.
- ItemEstimation of Vegetative Growth in Strawberry Plants Using Mobile LiDAR Laser Scanner(Basel : MDPI, 2022) Saha, Kowshik Kumar; Tsoulias, Nikos; Weltzien, Cornelia; Zude-Sasse, ManuelaMonitoring of plant vegetative growth can provide the basis for precise crop manage-ment. In this study, a 2D light detection and ranging (LiDAR) laser scanner, mounted on a linear conveyor, was used to acquire multi-temporal three-dimensional (3D) data from strawberry plants (‘Honeoye’ and ‘Malling Centenary’) 14–77 days after planting (DAP). Canopy geometrical variables, i.e., points per plant, height, ground projected area, and canopy volume profile, were extracted from 3D point cloud. The manually measured leaf area exhibited a linear relationship with LiDAR-derived parameters (R2 = 0.98, 0.90, 0.93, and 0.96 with number of points per plant, volume, height, and projected canopy area, respectively). However, the measuring uncertainty was high in the dense canopies. Particularly, the canopy volume estimation was adapted to the plant habitus to remove gaps and empty spaces in the canopy point cloud. The parametric values for maximum point to point distance (Dmax) = 0.15 cm and slice height (S) = 0.10 cm resulted in R2 = 0.80 and RMSPE = 26.93% for strawberry plant volume estimation considering actual volume measured by water displacement. The vertical volume profiling provided growth data for cultivars ‘Honeoye’ and ‘Malling Centenary’ being 51.36 cm3 at 77 DAP and 42.18 cm3 at 70 DAP, respectively. The results contribute an approach for estimating plant geometrical features and particularly strawberry canopy volume profile based on LiDAR point cloud for tracking plant growth.
- ItemApplication of hue spectra fingerprinting during cold storage and shelf-life of packaged sweet cherry(Cham : Springer, 2020) Le Nguyen, Lien Phuong; Visy, Anna; Baranyai, László; Friedrich, László; Mahajan, Pramod V.Presented work investigated the application of a new color analysis technique in post-harvest life of sweet cherry (Prunus avium L. ‘Hudson’). The hue spectra fingerprinting creates a histogram of image colors by summarizing the saturation. The advantage of this calculation method is that vivid colors make peaks while neutral background color is eliminated without object segmentation. Partial Least Squares (PLS) regression was used to estimate reference parameters during 9 d cold storage at 10 ± 0.5 °C (RH = 90 ± 1%) and following 2 d shelf-life at 20 ± 0.5 °C. The reference parameters of respiration, weight loss, fruit firmness and total soluble solid (TSS) content were measured. Samples were split into seven groups according to the number of perforations of polypropylene film and fructose concentration of moisture absorber. It was observed that parameters TSS and fruit firmness were the most sensitive to the length of storage. Weight loss was affected significantly by packaging. All reference parameters were estimated by PLS model with R2 > 0.917, but weight loss and respiration obtained high estimation error of RMSE% = 48.02% and 11.76%, respectively. TSS and fruit firmness prediction were successful with RMSE% = 0.84% and 1.85%, respectively. Desiccation and color change of peduncle became visible in the green range of hue spectra. Color change of red fruit was observed with decreasing saturation in the red range of hue spectra. Our findings suggest that hue spectra fingerprinting can be a useful nondestructive method for monitoring quality change of sweet cherry during post-harvest handling and shelf-life. © 2020, The Author(s).
- ItemA Pixel-wise Segmentation Model to Identify Bur Chervil (Anthriscus caucalis M. Bieb.) Within Images from a Cereal Cropping Field(Berlin ; Heidelberg : Springer, 2022) Karimi, Hadi; Navid, Hossein; Dammer, Karl-HeinzBecause of insufficient effectiveness after herbicide application in autumn, bur chervil (Anthriscus caucalis M. Bieb.) is often present in cereal fields in spring. A second reason for spreading is the warm winter in Europe due to climate change. This weed continues to germinate from autumn to spring. To prevent further spreading, a site-specific control in spring is reasonable. Color imagery would offer cheap and complete monitoring of entire fields. In this study, an end-to-end fully convolutional network approach is presented to detect bur chervil within color images. The dataset consisted of images taken at three sampling dates in spring 2018 in winter wheat and at one date in 2019 in winter rye from the same field. Pixels representing bur chervil were manually annotated in all images. After a random image augmentation was done, a Unet-based convolutional neural network model was trained using 560 (80%) of the sub-images from 2018 (training images). The power of the trained model at the three different sampling dates in 2018 was evaluated at 141 (20%) of the manually annotated sub-images from 2018 and all (100%) sub-images from 2019 (test images). Comparing the estimated and the manually annotated weed plants in the test images the Intersection over Union (Jaccard index) showed mean values in the range of 0.9628 to 0.9909 for the three sampling dates in 2018, and a value of 0.9292 for the one date in 2019. The Dice coefficients yielded mean values in the range of 0.9801 to 0.9954 for 2018 and a value of 0.9605 in 2019.
- ItemImproving Deep Learning-based Plant Disease Classification with Attention Mechanism(Berlin ; Heidelberg : Springer, 2022) Alirezazadeh, Pendar; Schirrmann, Michael; Stolzenburg, FriederIn recent years, deep learning-based plant disease classification has been widely developed. However, it is challenging to collect sufficient annotated image data to effectively train deep learning models for plant disease recognition. The attention mechanism in deep learning assists the model to focus on the informative data segments and extract the discriminative features of inputs to enhance training performance. This paper investigates the Convolutional Block Attention Module (CBAM) to improve classification with CNNs, which is a lightweight attention module that can be plugged into any CNN architecture with negligible overhead. Specifically, CBAM is applied to the output feature map of CNNs to highlight important local regions and extract more discriminative features. Well-known CNN models (i.e. EfficientNetB0, MobileNetV2, ResNet50, InceptionV3, and VGG19) were applied to do transfer learning for plant disease classification and then fine-tuned by a publicly available plant disease dataset of foliar diseases in pear trees called DiaMOS Plant. Amongst others, this dataset contains 3006 images of leaves affected by different stress symptoms. Among the tested CNNs, EfficientNetB0 has shown the best performance. EfficientNetB0+CBAM has outperformed EfficientNetB0 and obtained 86.89% classification accuracy. Experimental results show the effectiveness of the attention mechanism to improve the recognition accuracy of pre-trained CNNs when there are few training data.
- ItemEffect of In Vitro Digestion on the Antioxidant and Angiotensin-Converting Enzyme Inhibitory Potential of Buffalo Milk Processed Cheddar Cheese(Basel : MDPI AG, 2021) Shaukat, Amal; Nadeem, Muhammad; Qureshi, Tahir Mahmood; Kanwal, Rabia; Sultan, Muhammad; Kashongwe, Olivier Basole; Shamshiri, Redmond R.; Murtaza, Mian AnjumThe purpose of this study was to develop an in-vitro digestion protocol to evaluate the antioxidant potential of the peptides found in processed cheddar cheese using digestion enzymes. We first studied antioxidant and angiotensin-converting enzyme (ACE) inhibition and antioxidant activities of processed cheddar cheese with the addition of spices e.g., cumin, clove, and black pepper made from buffalo milk and ripened for 9 months. Then we conducted an in vitro digestion of processed cheddar cheese by gastric and duodenal enzymes. Freeze-dried water (WSE) and ethanol-soluble fractions (ESE) of processed cheddar cheese were also monitored for their ACE inhibition activity and antioxidant activities. In our preliminary experiments, different levels of spices (cumin, clove, and black pepper) were tested into a cheese matrix and only one level 0.2 g/100 g (0.2%) based on cheese weight was considered good after sensory evaluation. Findings of the present study revealed that ACE-inhibitory potential was the highest in processed cheese made from buffalo milk with the addition of 0.2% cumin, clove, and black pepper. A significant increase in ACE-inhibition (%) of processed cheddar cheese, as well as its WSE and ESE, was obtained. Lower IC50 values were found after duodenal phase digestion compared to oral phase digestion.
- ItemEffects of Pre-Processing Hot-Water Treatment on Aroma Relevant VOCs of Fresh-Cut Apple Slices Stored in Sugar Syrup(Basel : MDPI AG, 2020) Rux, Guido; Efe, Efecan; Ulrichs, Christian; Huyskens-Keil, Susanne; Hassenberg, Karin; Herppich, Werner B.In practice, fresh-cut fruit and fruit salads are currently stored submerged in sugar syrup (approx. 20%) to prevent browning, to slow down physiological processes and to extend shelf life. To minimize browning and microbial spoilage, slices may also be dipped in a citric acid/ascorbic acid solution for 5 min before storage in sugar syrup. To prevent the use of chemicals in organic production, short-term (30 s) hot-water treatment (sHWT) may be an alternative for gentle sanitation. Currently, profound knowledge on the impact of both sugar solution and sHWT on aroma and physiological properties of immersed fresh-cuts is lacking. Aroma is a very important aspect of fruit quality and generated by a great variety of volatile organic compounds (VOCs). Thus, potential interactive effects of sHWT and sugar syrup storage on quality of fresh-cut apple slices were evaluated, focusing on processing-induced changes in VOCs profiles. Intact ’Braeburn’ apples were sHW-treated at 55 °C and 65 °C for 30 s, sliced, partially treated with a commercial ascorbic/citric acid solution and slices stored in sugar syrup at 4 °C up to 13 days. Volatile emission, respiration and ethylene release were measured on storage days 5, 10 and 13. The impact of sHWT on VOCs was low while immersion and storage in sugar syrup had a much higher influence on aroma. sHWT did not negatively affect aroma quality of products and may replace acid dipping.