Proceedings
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| Filter results10 paper(s) found. |
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1. Precision Agriculture Initiative for Karnataka – A New Direction for Strengthening Farming CommunityStrengthening agriculture is crucial to meet the myriad challenges of rural poverty, food security, unemployment, and sustainability of natural resources and it also needs strengthening at technical, financial and management levels. In this context... U.K. Shanwad, M.B. Patil, V. H, M. B.g , P. R, R. N.l. , S. S, R. Khosla, V.C. Patil |
2. NDVI 'Depression' In Pastures Following GrazingPasture biomass estimation from normalized difference vegetation index (NDVI) using ground, air or space borne sensors is becoming more widely used in precision agriculture. Proximal active optical sensors (AOS) have the potential to eliminate the confounding effects of path radiance and target illumination conditions typically encountered using passive sensors. Any algorithm that infers the green fraction of pasture from NDVI must factor in plant morphology and live/dead plant ratio, irrespective... J.S. Stanley, D.W. Lamb, M.G. Trotter, M.M. Rahman |
3. Strategies For Scientific Communication Of Precision Agriculture In BrazilScientific knowledge popularization is the way to the society access technical scientific advances. The challenge is to increase the means, channels and processes of information and relationship with society and decode scientific issues into a format that makes knowledge accessible. The Embrapa Precision Agriculture Network has been used scientific communication strategies at the traditional and new media, as a way of approach with various stakeholders, contributing to the construction... C.V. fragalle, J.C. Silva, E.P. fragalle, R.Y. Inamasu, A.C. Bernardi |
4. Precision Placement of Corn Gluten Meal for Weed Control in Organic Vegetable ProductionOrganic vegetable producers rank weeds as one of their most troublesome, time consuming, and costly production problems. As a result of the limited number of organically approved weed control herbicides, the precision placement of these materials increases their potential usefulness in organic production systems. As a non-selective preemergence or preplant-incorporated herbicide, corn gluten meal (CGM) inhibits root development; decreases shoot length, and reduces plant survival. The development... C.L. Webber iii, M.J. Taylor, J.W. Shrefler |
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. Identifying Key Factors Influencing Yield Spatial Pattern and Temporal Stability for Management Zone DelineationManagement zone delineation is a practical strategy for site-specific management. Numerous approaches have been used to identify these homogenous areas in the field, including approaches using multiple years of historical yield maps. However, there are still knowledge gaps in identifying variables influencing spatial and temporal variability of crop yield that should be used for management zone delineation. The objective of this study is to identify key soil and landscape properties affecting... L.N. Lacerda, Y. Miao, K. Mizuta, K. Stueve |
7. Potential of UAS Multispectral Imagery for Predicting Yield Determining Physiological Parameters of CottonThe use of unmanned aerial systems (UAS) in precision agriculture has increased rapidly due to the availability of reliable, low-cost, and high-resolution sensors as well as advanced image processing software. Lint yield in cotton is the product of three physiological parameters: photosynthetically active radiation intercepted by canopy (IPAR), the efficiency of converting intercepted active radiation to biomass (RUE), and the ratio of economic yield to total dry matter (HI). The relationships... A. Pokhrel, S. Virk, J.L. Snider, G. Vellidis, V. Parkash |
8. Cloud Correction of Sentinel-2 NDVI Using S2cloudless PackageOptical satellite-derived Normalized Difference Vegetation Index (NDVI) is by far the most commonly used vegetation index value for crop monitoring. However, it is quite sensitive to the cloud, and cloud shadows and significantly decreases its usability, especially in agricultural applications. Therefore, an accurate and reliable cloud correction method is mandatory for its effective application. To address this issue, we have developed an approach to correct the NDVI values of each and every... A. Saxena, M. Dash, A.P. Verma |
9. Within-field Spatial Variability in Optimal Sulfur Rates for Corn in Minnesota: Implications for Precision Sulfur ManagementThe ongoing decline in sulfur (S) atmospheric depositions and high yield crop production have resulted in S deficiency and the need for S fertilizer applications in corn cropping systems. Many farmers are applying S fertilizers uniformly across their fields. Little has been reported on the within-field spatial variability in optimal S rates and the potential benefits of variable rate S applications. The objectives of this study were to 1) assess within-field variability of optimal S rates (OSR),... R.P. Negrini, Y. Miao, K. Mizuta, K. Stueve, D. Kaiser, J.A. Coulter |
10. Revolutionizing Poultry Health: AI-Powered Real-Time Disease Detection Using YOLO v7 and IQR for Enhanced Farm ProductivityPrompt and accurate detection of poultry diseases is crucial to prevent outbreaks and reduce economic losses. Conventional monitoring systems based on manual inspections are inefficient and prone to error, delaying timely interventions. This study proposes an AI-driven early warning system that integrates YOLO v7 for real-time image detection with Hampel Filters for anomaly recognition. The model specifically targets two critical health indicators: rooster combs and eyes. Over a period of 53 days... A. Santosa |