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| Filter results8 paper(s) found. |
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1. Invasive and Non-Invasive Technology for Measuring Water Content of Crop Leaves in Greenhouse HorticultureMoisture status in the crop is closely related to various physiological activities of the crop. If we can measure the moisture status in the crop in real time, we can understand the photosynthetic activity, which is an important physiological activity for growing crops, and the movement of the product from photosynthesis. Therefore, we verified it is possible to measure water content of crop leaves nondestructively using invasive method and non-invasive method. As a non-invasive measurement method,... H. Umeda, K. Muramatsu, Y. Kawagoe, T. Sugihara, S. Shibusawa, Y. Iwasaki |
2. Using a Fully Convolutional Neural Network for Detecting Locations of Weeds in Images from Cereal FieldsInformation about the presence of weeds in fields is important to decide on a weed control strategy. This is especially crucial in precision weed management, where the position of each plant is essential for conducting mechanical weed control or patch spraying. For detecting weeds, this study proposes a fully convolutional neural network, which detects weeds in images and classifies each one as either a monocot or dicot. The network has been trained on over 13 000 weed annotations... M. Dyrmann, S. Skovsen, R.N. Jørgensen, M.S. Laursen |
3. Predicting Dry Matter Composition of Grass Clover Leys Using Data Simulation and Camera-Based Segmentation of Field Canopies into White Clover, Red Clover, Grass and WeedsTargeted fertilization of grass clover leys shows high financial and environmental potentials leading to higher yields of increased quality, while reducing nitrate leaching. To realize the gains, an accurate fertilization map is required, which is closely related to the local composition of plant species in the biomass. In our setup, we utilize a top-down canopy view of the grass clover ley to estimate the composition of the vegetation, and predict the composition of the dry matter of the forage.... S. Skovsen, M. Dyrmann, J. Eriksen, R. Gislum, H. Karstoft, R.N. Jørgensen |
4. Spatial Variability of Optimized Herbicide Mixtures and DosagesDriven by 25 years of Danish, political 'pesticide action plans', aiming at reducing the use of pesticides, a Danish Decision Support System (DSS) for Integrated Weed Management (IWM) has been constructed. This online tool, called ‘IPMwise’ is now in its 4th generation. It integrates the 8 general IPM-principles as defined by the EU. In Denmark, this DSS includes 30 crops, 105 weeds and full assortments of herbicides. Due to generic qualities in both the integrated... P. Rydahl, R.N. Jorgensen, M. Dyrmann, N. Jensen, M.D. Sorensen, O.M. Bojer, P. Andersen |
5. Data Power: Understanding the Impacts of Precision Agriculture on Social RelationsPrecision agriculture has been greatly promoted for the potential of these technologies to sustainably intensify food production through increasing yields and profits, decreasing the environmental impacts of production, and improving food safety and transparency in the food system through the data collected by precision agriculture technologies. However, little attention has been given to the potential of these technologies to impact social relations within the agricultural industry. ... E. Duncan, E. Fraser |
6. Application of Routines for Automation of Geostatistical Analysis Procedures and Interpolation of Data by Ordinary KrigingOrdinary kriging (OK) is one of the most suitable interpolation methods for the construction of thematic maps used in precision agriculture. However, the use of OK is complex. Farmers/agronomists are generally not highly trained to use geostatistical methods to produce soil and plant attribute maps for precision agriculture and thus ensure that best management approaches are used. Therefore, the objective of this work was to develop and apply computational routines using procedures and geostatistical... N.M. Betzek, E.G. Souza, C.L. Bazzi, P.G. Magalhães, A. Gavioli, K. Schenatto, R.W. Dall'agnol |
7. Machine Learning Algorithms in Detecting Long-term Effect of Climatic Factors for Alfalfa Production in KansasThe water levels of the Ogallala Aquifer are depleting so much that agricultural land returns in Kansas are expected to drop by $34.1 million by 2050. It is imperative to understand how frequent droughts and the contrasting rates of groundwater withdrawal and recharge are affected by climate shifts in Kansas. Alfalfa, the ‘Queen of Forages’, is a water demanding crop which supplies high nutritional feed for beef industry that offered Kansas producers a $500 million production value... F. Nazrul, J. Kim, S. Dey, S. Palla, D. Sihi, B. Whitaker, G. Jha |
8. Prediction of Field-scale Evapotranspiration Using Process Based Modeling and Geostatistical Time-series InterpolationIrrigation scheduling depends on the combination of evaporative demand from the atmosphere, spatial and temporal heterogeneity in soil properties and changes in crop canopy during a growing season. This on-farm trial is based on data collected in 72-acre processing tomato field in Central Valley of California. The Multiband Spectrometric Arable Mark 2 sensors at three different locations in the field. Multispectral and thermal imagery provided by Ceres Imaging were collected eight times during... G. Jha, F. Nazrul, M. Nocco, M. Pagé fortin, B. Whitaker, D. Diaz, A. Gal, R. Schmidt |