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- ItemSpatial Distribution Patterns for Identifying Risk Areas Associated with False Smut Disease of Rice in Southern India(Basel : MDPI, 2022) Huded, Sharanabasav; Pramesh, Devanna; Chittaragi, Amoghavarsha; Sridhara, Shankarappa; Chidanandappa, Eranna; Prasannakumar, Muthukapalli K.; Manjunatha, Channappa; Patil, Balanagouda; Shil, Sandip; Pushpa, Hanumanthappa Deeshappa; Raghunandana, Adke; Usha, Indrajeet; Balasundram, Siva K.; Shamshiri, Redmond R.False smut disease (FSD) of rice incited by Ustilaginoidea virens is an emerging threat to paddy cultivation worldwide. We investigated the spatial distribution of FSD in different paddy ecosystems of South Indian states, viz., Andhra Pradesh, Karnataka, Tamil Nadu, and Telangana, by considering the exploratory data from 111 sampling sites. Point pattern and surface interpolation analyses were carried out to identify the spatial patterns of FSD across the studied areas. The spatial clusters of FSD were confirmed by employing spatial autocorrelation and Ripley’s K function. Further, ordinary kriging (OK), indicator kriging (IK), and inverse distance weighting (IDW) were used to create spatial maps by predicting the values at unvisited locations. The agglomerative hierarchical cluster analysis using the average linkage method identified four main clusters of FSD. From the Local Moran’s I statistic, most of the areas of Andhra Pradesh and Tamil Nadu were clustered together (at I > 0), except the coastal and interior districts of Karnataka (at I < 0). Spatial patterns of FSD severity were determined by semi-variogram experimental models, and the spherical model was the best fit. Results from the interpolation technique, the potential FSD hot spots/risk areas were majorly identified in Tamil Nadu and a few traditional rice-growing ecosystems of Northern Karnataka. This is the first intensive study that attempted to understand the spatial patterns of FSD using geostatistical approaches in India. The findings from this study would help in setting up ecosystem-specific management strategies to reduce the spread of FSD in India.
- ItemOptical Spectrometry to Determine Nutrient Concentrations and other Physicochemical Parameters in Liquid Organic Manures: A Review(Basel : MDPI, 2022) Horf, Michael; Vogel, Sebastian; Drücker, Harm; Gebbers, Robin; Olfs, Hans-WernerNutrient concentrations in livestock manures and biogas digestates show a huge variability due to disparities in animal husbandry systems concerning animal species, feed composition, etc. Therefore, a nutrient estimation based on recommendation tables is not reliable when the exact chemical composition is needed. The alternative, to analyse representative fertilizer samples in a standard laboratory, is too time-and cost-intensive to be an accepted routine method for farmers. However, precise knowledge about the actual nutrient concentrations in liquid organic fertilizers is a prerequisite to ensure optimal nutrient supply for growing crops and on the other hand to avoid environmental problems caused by overfertilization. Therefore, spectrometric methods receive increasing attention as fast and low-cost alternatives. This review summarizes the present state of research based on optical spectrometry used at laboratory and field scale for predicting several parameters of liquid organic manures. It emphasizes three categories: (1) physicochemical parameters, e.g., dry matter, pH, and electrical conductivity; (2) main plant nutrients, i.e., total nitrogen, ammonium nitrogen, phosphorus, potassium, magnesium, calcium, and sulfur; and (3) micronutrients, i.e., manganese, iron, copper, and zinc. Furthermore, the commonly used sample preparation techniques, spectrometer types, measuring modes, and chemometric methods are presented. The primarily promising scientific results of the last 30 years contributed to the fact that near-infrared spectrometry (NIRS) was established in commercial laboratories as an alternative method to wet chemical standard methods. Furthermore, companies developed technical setups using NIRS for on-line applications of liquid organic manures. Thus, NIRS seems to have evolved to a competitive measurement procedure, although parts of this technique still need to be improved to ensure sufficient accuracy, especially in quality management.
- ItemIdentification of representative dairy cattle and fodder crop production typologies at regional scale in Europe(Berlin ; Heidelberg : Springer, 2022) Díaz de Otálora, Xabier; Dragoni, Federico; Del Prado, Agustín; Estellés, Fernándo; Wilfart, Aurélie; Krol, Dominika; Balaine, Lorraine; Anestis, Vasileios; Amon, BarbaraEuropean dairy production faces significant economic, environmental, and social sustainability challenges. Given the great diversity of dairy cattle production systems in Europe, region-specific concepts to improve environmental and socioeconomic sustainability are needed. Regionally integrated dairy cattle-crop systems emerge as a more resilient and sustainable alternative to highly specialized farming systems. Identifying different dairy cattle production typologies and their potential interactions with fodder crop production is presented as a step in transitioning to optimized agricultural systems. Currently existing typologies of integrated systems are often insufficient when characterizing structural, socioeconomic, and environmental components of farms. We fill this gap in the literature by identifying, describing, and comparing representative dairy cattle production system typologies and their interrelation with regional fodder crop production at the European regional scale. This is a necessary step to assess the scope for adapted mitigation and sustainability measures in the future. For this purpose, a multivariate statistical approach is applied. We show how different land-use practices, farm structure characteristics, socio-economic attributes, and emission intensities condition dairy production. Furthermore, the diversity of regional fodder crop production systems is demonstrated by analyzing their distribution in Europe. Together with identified typologies, varying degrees of regional specialization in milk production allow for identifying future strategies associated with the application of integrated systems in key European dairy regions. This study contributes to a better understanding of the existing milk production diversity in Europe and their relationship with regional fodder crop production. In addition, we discuss the benefits of integrated systems as a clear, viable, and resilient alternative to ongoing livestock intensification in the European context. Identifying interactions between components of integrated systems will facilitate decision-making, the design and implementation of measures to mitigate climate change, and the promotion of positive socio-economic and environmental interactions.
- ItemComparison of Methane Emission Patterns from Dairy Housings with Solid and Slatted Floors at Two Locations(Basel : MDPI, 2022) Hempel, Sabrina; Janke, David; Losand, Bernd; Zeyer, Kerstin; Zähner, Michael; Mohn, Joachim; Amon, Thomas; Schrade, SabineMethane (CH4) emissions from dairy husbandry are a hot topic in the context of active climate protection, where housing systems with slatted floors and slurry storage inside are in general expected to emit more than systems with solid floors. There are multiple factors, including climate conditions, that modulate the emission pattern. In this study, we investigated interrelations between CH4 emission patterns and climate conditions as well as differences between farm locations versus floor effects. We considered three data sets with 265, 264 and 275 hourly emission values from two housing systems (one slatted, one solid floor) in Switzerland and one system with solid floors in Germany. Each data set incorporated measurements in summer, winter and a transition season. The average CH4 emission was highest for the slatted floor system. For the solid floor systems, CH4 emissions at the Swiss location were around 30% higher compared to the German location. The shape of the distributions for the two solid floor systems was rather similar but very different from the distribution for the slatted floor system, which showed higher prevalence for extreme emissions. Rank correlations, which measure the degree of similarity between two rankings in terms of linear relation, were not able to detect dependencies at the selected significance level. In contrast, mutual information, which measures more general statistical dependencies in terms of shared information, revealed highly significant dependencies for almost all variable pairs. The weakest statistical relation was found between winds speed and CH4 emission, but the convection regime was found to play a key role. Clustering was consistent among the three data sets with five typical clusters related to high/low temperature and wind speed, respectively, as well as in some cases to morning and evening hours. Our analysis showed that despite the disparate and often insignificant correlation between environmental variables and CH4 emission, there is a strong relation between both, which shapes the emission pattern in many aspects much more in addition to differences in the floor type. Although a clear distinction of high and low emission condition clusters based on the selected environmental variables was not possible, trends were clearly visible. Further research with larger data sets is advisable to verify the detected trends and enable prognoses for husbandry systems under different climate conditions.
- ItemDeep Learning Object Detection for Image Analysis of Cherry Fruit Fly (Rhagoletis cerasi L.) on Yellow Sticky Traps(Berlin ; Heidelberg : Springer, 2022) Salamut, Christian; Kohnert, Iris; Landwehr, Niels; Pflanz, Michael; Schirrmann, Michael; Zare, MohammadInsect populations appear with a high spatial, temporal and type-specific diversity in orchards. One of the many monitoring tools for pest management is the manual assessment of sticky traps. However, this type of assessment is laborious and time-consuming so that only a few locations can be controlled in an orchard. The aim of this study is to test state-of-the art object detection algorithms from deep learning to automatically detect cherry fruit flies (Rhagoletis cerasi), a common insect pest in cherry plantations, within images from yellow sticky traps. An image annotation database was built with images taken from yellow sticky traps with more than 1600 annotated cherry fruit flies. For better handling in the computational algorithms, the images were augmented to smaller ones by the known image preparation methods “flipping” and “cropping” before performing the deep learning. Five deep learning image recognition models were tested including Faster Region-based Convolutional Neural Network (R-CNN) with two different methods of pretraining, Single Shot Detector (SSD), RetinaNet, and You Only Look Once version 5 (YOLOv5). R‑CNN and RetinaNet models outperformed other ones with a detection average precision of 0.9. The results indicate that deep learning can act as an integral component of an automated system for high-throughput assessment of pest insects in orchards. Therefore, this can reduce the time for repetitive and laborious trap assessment but also increase the observed amount of sticky traps
- 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.
- 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.
- 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.
- ItemEffects of increasing air temperature on skin and respiration heat loss from dairy cows at different relative humidity and air velocity levels(Savoy, Ill. : ADSA, 2022) Zhou, M.; Huynh, T.T.T.; Groot Koerkamp, P.W.G.; van Dixhoorn, I.D.E.; Amon, T.; Aarnink, A.J.A.The focus of this study was to identify the effects of increasing ambient temperature (T) at different relative humidity (RH) and air velocity (AV) levels on heat loss from the skin surface and through respiration of dairy cows. Twenty Holstein dairy cows with an average parity of 2.0 ± 0.7 and body weight of 687 ± 46 kg participated in the study. Two climate-controlled respiration chambers were used. The experimental indoor climate was programmed to follow a diurnal pattern with ambient T at night being 9°C lower than during the day. Night ambient T was gradually increased from 7 to 21°C and day ambient T was increased from 16 to 30°C within an 8-d period, both with an incremental change of 2°C per day. A diurnal pattern for RH was created as well, with low values during the day and high values during the night (low: RH_l = 30-50%; medium: RH_m = 45-70%; and high: RH_h = 60-90%). The effects of AV were studied during daytime at 3 levels (no fan: AV_l = 0.1 m/s; fan at medium speed: AV_m = 1.0 m/s; and fan at high speed: AV_h = 1.5 m/s). The AV_m and AV_h were combined only with RH_m. In total, there were 5 treatments with 4 replicates (cows) for each. Effects of short and long exposure time to warm condition were evaluated by collecting data 2 times a day, in the morning (short: 1-h exposure time) and afternoon (long: 8-h exposure time). The cows were allowed to adapt to the experimental conditions during 3 d before the main 8-d experimental period. The cows had free access to feed and water. Sensible heat loss (SHL) and latent heat loss (LHL) from the skin surface were measured using a ventilated skin box placed on the belly of the cow. These heat losses from respiration were measured with a face mask covering the cow's nose and mouth. The results showed that skin SHL decreased with increasing ambient T and the decreasing rate was not affected by RH or AV. The average skin SHL, however, was higher under medium and high AV levels, whereas it was similar under different RH levels. The skin LHL increased with increasing ambient T. There was no effect of RH on the increasing rate of LHL with ambient T. A larger increasing rate of skin LHL with ambient T was observed at high AV level compared with the other levels. Both RH and AV had no significant effects on respiration SHL or LHL. The cows lost more skin sensible heat and total respiration heat under long exposure than short exposure. When ambient T was below 20°C the total LHL (skin + respiration) represented approx. 50% of total heat loss, whereas above 28°C the LHL accounted for more than 70% of the total heat loss. Respiration heat loss increased by 34 and 24% under short and long exposures when ambient T rose from 16 to 32°C.
- ItemAnnual shoot growth on apple trees with variable canopy leaf area and crop load in response to LiDAR scanned leaf area to fruit ratio(Lublin : IA PAS, 2022) Penzel, Martin; Tsoulias, NikosIn tree fruit crops, the crop load is one factor that has an influence on the vegetative growth of the trees. However, since trees vary in leaf area and associated fruit bearing capacity, the number of fruit per tree alone is not sufficient to predict their vegetative growth. In the present study, it was investigated whether the leaf area to fruit ratio of trees variable in size and crop load, measured automatically with a LiDAR laser scanner, have an influence on growth properties of the annual shoots. Canopy leaf area, the number of fruit per tree and the leaf area to fruit ratio of apple trees from two commercial apple orchards of the cultivar 'Gala' grown on sandy soils were scanned with a LiDAR laser scanner over a two-year period (n=12 trees per orchard and year). Additionally, the amount of carbon partitioned to fruit and annual shoot growth was quantified for each tree in both years (n=36). No correlation between the number of fruit per tree and the canopy leaf area alone to the amount of carbon partitioned to annual shoot growth was found in both orchards. However, the carbon partitioned to fruit correlated to the leaf area to fruit ratio, while the amount of carbon partitioned to the annual shoot growth was only correlated to the leaf area to fruit ratio in the young orchard. The inter-tree variability in shoot properties has been described. Nevertheless, it was found that the leaf area to fruit ratio is a weak indicator for shoot properties in apple trees, especially in the mature orchards.