Proceedings
Authors
| Filter results8 paper(s) found. |
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1. Estimation of Rice Yield from MODIS Data in West Java, IndonesiaChiharu Hongo1*, Takaaki Furukawa1, Gunardi Sigit2, Masayasu Maki3, Koki Honma3,... C. Hongo, T. Furukawa, G. Sigit, M. Maki, K. Honma, K. Yoshida, K. Oki, H. Shirakawa |
2. Estimating Spatial Variation In Annual Pasture YieldYield mapping is an essential tool for precision management of arable crops. Crop yields can be measured once, at harvest, automatically by the harvesting machinery, and be used to inform a wide range of activities. However yield mapping has had minimal adoption by pastoral farmers. Yield mapping is also a potentially valuable tool for precision management of pastures. However it is difficult to practically map yields on pastures, as they... S.J. Dennis, W. Clarke-hill, A. Taylor, R. Dynes, K. O'neill, T. Jowett |
3. Using Deep Learning - Convolutional Naural Networks (CNNS) for Real-Time Fruit Detection in the TreeImage/video processing for fruit detection in the tree using hard-coded feature extraction algorithms have shown high accuracy on fruit detection during recent years. While accurate, these approaches even with high-end hardware are still computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks architecture based on single-stage detectors. Using deep-learning techniques eliminates the need for hard-code specific features for specific... K. Bresilla, L. Manfrini, A. Boini, G. Perulli, B. Morandi, L.C. Grappadelli |
4. Through the Grass Ceiling: Using Multiple Data Sources on Intra-Field Variability to Reset Expectations of Pasture Production and Farm ProfitabilityIntra-field variability has received much attention in arable and horticultural contexts. It has resulted in increased profitability as well as reduced environmental footprint. However, in a pastoral context, the value of understanding intra-field variability has not been widely appreciated. In this programme, we used available technologies to develop multiple data layers on multiple fields within a dairy farm. This farm was selected as it was already performing at a high level, with well-developed... W. King, R. Dynes, S. Laurenson, S. Zydenbos, R. Macauliffe, A. Taylor, M. Manning, A. Roberts, M. White |
5. Calculating the Water Deficit of Apple Orchard by Means of Spatially Resolved ApproachIn semi-humid climate, spatially resolved analysis of water deficit was carried out in apple orchard (Malus x domestica 'Pinova'). The meteorological data were recorded daily by a weather station. The apparent soil electrical conductivity (ECa) was measured at field capacity, and twenty soil samples in 30 cm were gathered for texture, bulk density, and gravimetric soil water content analyses. Furthermore, ten trees were defoliated in different ECa regions in order to estimate the leaf... N. Tsoulias, D. Paraforos, N. Brandes, S. Fountas, M. Zude-sasse |
6. Mapping Soil Health and Grain Quality Variations Across a Corn Field in TexasSoil health is a key property of soils influencing grain yield and quality. Within-field mapping of soil health index and grain quality can help farmers and managers to adjust site-specific farm management decisions for economic benefits. A study was conducted to map within-field soil health and grain protein and oil content variations using apparent electrical conductivity (ECa) and terrain attributes as their predictors. Two hundred and two topsoil samples were analyzed to determine soil health... K. Adhikari, D.R. Smith, C. Hajda, P.R. Owens |
7. Impact of Soil Heterogeneity and Precision Air Seed Drill Settings on Maize Emergence Uniformity: Toward Sensor-based Predictive ModelsSoil spatial heterogeneity at the intra-field scale strongly constrains crop establishment. This heterogeneity in soil texture, strength, and moisture often leads to uneven seed-soil contact and inconsistent emergence, reducing yield potential. Extreme climatic conditions, such as soil saturation or drought, amplify these challenges and further compromise uniform crop emergence. Although this issue is widely acknowledged, accounting for soil heterogeneity remains poorly integrated in... A. Veiga, H. De Araujo, L. Tetard, M. Faucon, C. Ugarte |
8. Digital Agriculture in Decision-Making for Sustainable Disease Management in Soybean CropsSoybean (Glycine max L.) stands out as one of the main crops of agronomic interest, widely used in human and animal nutrition due to its high protein content and diversity of derivatives. Soybean crop productivity is strongly influenced by meteorological conditions, adopted management practices, and the incidence of pathogens, which can significantly reduce the plant’s photosynthetically active area, directly impacting final yield. In this context, the present study aimed to evaluate... E. Rolim Farias Da Silva, L. Lüdtke, G. Ductra Bortolotti, I. Maldaner, L. , J. Sgarbossa, L. Silveira Pavão, A. Müllich |