Proceedings
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| Filter results4 paper(s) found. |
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1. Response of Soybean Cultivars According to Management Zones in Southern BrazilThe positioning of soybean cultivars on fields according your environmental response is new strategy to obtain high soybean yields. The aim of this study was to investigate the agronomic response of six soybean cultivars according management zones in Southern Brazil. The study was conducted in 2013/2014 and in two fields located in Boa Vista das Missões, Rio Grande do Sul, Brazil. The experimental design was a randomized complete block in a factorial arrangement (3x6), with three management... T.J. Amado, A.L. Santi, G.M. Corassa, M.B. Bisognin, R. Gaviraghi, J.L. Pires |
2. Variable-Rate-Fertilization of Phosphorus and Lime – Economic Effects and Maximum Allowed Costs for Small-Scale Soil AnalysisThe pH values and macro nutrient contents are characterised by considerable variance within a field. A constant-rate-fertilization, which is practiced at most farms, does not reduce this effect, it may even boost variance. Besides the suboptimal nutrient supply, the site-specific yield potential is not exploited. Constant-rate-fertilization and liming results in an inefficient utilisation by over- and undersupply of most of the areas within a field. Fertilization with lime and phosphorus causes... S. Schulte-ostermann, P. Wagner |
3. 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 |
4. 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 |