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| Filter results18 paper(s) found. |
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1. Spatial Variability of Soil Properties in Intensively Managed Tropical Grassland in BrazilFor the intensification of tropical grass pastures systems the soil fertility building up by liming and balanced fertilization is necessary. The knowledge of spatial variability soil properties is useful in the rational use of inputs, as in the variable rate application of lime and fertilizers. PA requires methods to indicate the spatial variability of soil and plant parameters. The objective of this work was to map and evaluate the soil properties and maps the site specific liming and fertil... G.M. Bettiol, R.Y. Inamasu, L.M. Rabello, A.C. Bernardi, M. Campana, P.P. Oliveira |
2. Influence Of Phosphorus Application With Or Without Nitrogen On Oat (Avena Sativa) Grass Nutritive Value And In Situ Digestion Kinetics In Buffalo BullsFodder is the mainstay of ruminant production in majority of developing countries. However, its low yield and poor quality are considered considerable constrains which impede ruminant productivity. Fodder production and its nutritive value can be enhanced by ensuring adequate supply and utilization of nutrien... M.U. Nisa, I. Babar, M. Sarwar, N.A. Tauqir, M.A. Shahzad |
3. Remote Collection of Behavioral and Physiological Data to Detect Lame CowsAuthors of abstract: C. Kamphuis, J. Burke, J. Jago ... J. Jago, J. Burke, C. Kamphuis, B. Dela rue |
4. Two On-Farm Tests to Evaluate In-Line Sensors for Mastitis DetectionTo date, there is no independent and uniformly presented information available regarding detection performance of automated in-line mastitis detection systems. This lack of information makes it hard for farmers ... B. Dela rue, J. Jago, C. Kamphuis |
5. Field Evaluation of Automated Estrus Detection Systems - Meeting Farmers' ExpectationAutomated systems for oestrus detection are commonly marketed as a suitable, or in some cases, a higher performing alternative to visual observation. Farmers, particularly those with larger herds relying on less experienced staff, view the perceived benefits of automated systems as both economic and physical, with expectations of improved oestrus detection efficiency with lower labour input. There is little evidence-based information available on the field performance of these systems to... B.T. Dela rue, C. Kamphuis, J.G. Jago, C.R. Burke |
6. Challenges and Opportunities for Precision Dairy Farming in New Zealand.A study was commissioned by DairyNZ, a dairy industry good organisation in New Zealand, to identify some of the key challenges and opportunities in the precision dairy space. In New Zealand there has been an increasing research focus on the use of information and communication technologies (ICT) ... I. Yule , C. Eastwood |
7. The Use of Sensing Technologies to Monitor and Track the Behavior of Cows on a Commercial Dairy FarmNew Zealand farmers are facing rapidly increasing pressure to reduce nutrient losses from their farming enterprises to the environment caused by grazing ruminants. ... I. Draganova, I. Yule, M. Stevenson |
8. Synchronized Windrow Intelligent Perception System (SWIPE)The practice of bale production, in forage agriculture, involves various machines that include tractors, tedders, rakers, and balers. As part of the baling process, silage material is placed in windrows, linearly raked mounds, to drive over with a baler for easy collection into bales. Traditionally, a baler is an implement that is attached on the back of a tractor to generate bales of a specific shape. Forage agricultural equipment manufacturers have recently released an operator driven, self... E.M. Dupont, P.R. Kolar |
9. Economics of Field Size for Autonomous Crop MachinesField size constrains spatial and temporal management of agriculture with implications for farm profitability, field biodiversity and environmental performance. Large, conventional equipment struggles to farm small, irregularly shaped fields efficiently. The study hypothesized that autonomous crop machines would make it possible to farm small non-rectangular fields profitably, thereby preserving field biodiversity and other environmental benefits. Using the experience of the Hands Free Hectar... A. Al amin, J. Lowenberg‑deboer, K. Franklin, K. Behrendt |
10. Seed Localization System Suite with CNNs for Seed Spacing Estimation, Population Estimation and DoublesProper seed placement during planting is critical to achieve uniform emergence which optimizes the crop for maximum yield potential. Currently, the ideal way to determine planter performance is to manually measure plant spacing and seeding depth. However, this process is both cost- and labor-intensive and prone to human errors. Therefore, this study aimed to develop seed localization system (SLS) system to measure seed spacing and seeding depth and providing the geo-location of each planted s... A. Sharda, R. Harsha chepally |
11. Agricultural Robots Classification Based on Clustering by Features and FunctionRobotic systems in agriculture (hereafter referred to as agrobots) have become popular in the last few years. They represent an opportunity to make food production more efficient, especially when coupled with technologies such as the Internet of Things and Big Data. Agrobots bring many advantages in farm operations: they can reduce humane fatigue and work-related accidents. In contrast, their large-scale diffusion is today limited by a lack of clarity and exhaustiveness in the regulatory fram... M. Canavari, M. Medici, G. Rossetti |
12. Agronomic Opportunities Highlighted by the Hands Free Hectare and Hands Free Farm Autonomous Farming ProjectsWith agriculture facing various challenges including population increase, urbanisation and both mitigating and managing climate change, agricultural automation and robotics have long been seen as potential solutions beyond precision farming. The Hands Free Hectare (HFH) and Hands Free Farm (HFF) collaborative projects based at Harper Adams University (HAU) have been developing autonomous farming systems since 2016 and have conducted multiple autonomous field crop production cycles since a wor... K.F. Franklin |
13. Possibilities for Improved Decision Making and Operating Efficiency Derived from the Predictability of Autonomous Farming OperationsFor the last 6 years, small autonomous agricultural vehicles have been operating on Harper Adams University’s fields in Shropshire. Starting with a single tractor on a single rectangular hectare (2.5 acres) and moving on to three tractors on 5 irregularly shaped fields covering over 30 hectares (75 acres). Multiple crops have been grown; planting, tending, and harvesting with autonomous tractors and harvesters. The fields are worked using a Controlled Traffic Farming s... M. Gutteridge |
14. Realising the Potential of Agricultural Robotics and AI: The Ethical ChallengesRecent advances in AI and robotics may dramatically transform agriculture by greatly expanding the number of contexts in which the techniques of precision agriculture may be applied. Inevitably, this next agricultural revolution will generate profound ethical issues: opportunities as well as risks. Clever applications of AI and robotics may allow agriculture to be more sustainable by facilitating more precise applications of water, fertilisers, and herbicides. Robots may take some of the drud... R. Sparrow |
15. Comparison of NDVI Values at Different Phenological Stages of Winter Wheat (Triticum Aestivum L.)The main objective of this study is to monitor, detect and quantify the presence of live green vegetation with the MicaSense RedEdge-MX Dual Camera System (MS) mounted on a DJI Matrice 210 V2 and GreenSeeker HCS 250 (GS) in winter wheat (Triticum aestivum L.) by using Normalized Difference Vegetation Index (NDVI). Surveys were conducted in the North-Western part of Hungary, in Mosonmagyaróvár on six different dates. A small-scale field trial in winter wheat was constructed as a ... S. Zsebő, G. Kukorelli, V. Vona, L. Bede, D. Stencinger, A. Kovacs, G. Milics, I.M. Kulmany, B. Horváth, G. Hegedűs, J.A. Abdinoor |
16. The Relationship Between Vegetation Indices Derived from UAV Imagery and Maturity Class in Potato Breeding TrialsIn potato breeding, maturity class (MC) is a crucial selection criterion because this is a critical aspect of commercial potato production. Currently, the classification of potato genotypes into MCs is done visually, which is time- and labor-consuming. Unmanned aerial vehicles (UAVs) equipped with sensors can acquire images with high spatial and temporal resolution. The objectives of this study were to 1) establish the relationship between vegetation indices (VIs) derived from UAV imagery at ... S.M. Samborski, U. Torres, R. Leszczyńska, A. Bech, M. Bagavathiannan |
17. Spectral Response of Six Treatments of Soil Fertilization in Potato (Solanum tuberosum L.) Var. Diacol Capiro with UASIn Colombia, potato cultivation occupies the third place among the transient crops in the country, covering approximately 160,000 hectares. It holds the first place in terms of production value, reaching US $500 million, and ranks as the second crop with the highest demand for fertilizers, constituting 20% of production costs. The departments of Cundinamarca, Boyacá, Nariño, and Antioquia are the primary potato producers, accounting for 87.8% of the total production. Traditional... S.A. Rubaino sosa, O.Y. Cristancho rojas, W.A. Leon rueda, O.G. Montero pinilla, J.C. Roa bello, I.A. Lizarazo salcedo |
18. Vegetation Coverage Specific Flower Density Estimation in Blackberry Using Unmanned Aerial Vehicle (UAV) Remote SensingThe effective management of agricultural systems relies on the utilization of accurate data collection techniques to analyze essential crop attributes to enhance productivity and ensure profits. Data collection procedures for specialty horticultural crops are mostly subjective, time consuming and may not be accurate for management decisions in both phenotypic studies and crop production. Reliable and repeatable standard methods are therefore needed to capture and calculate attributes of horti... A. Tagoe, C. Koparan, A. Poncet, D.M. Johnson, M. Worthington, D. Wang |