Proceedings
Authors
| Filter results12 paper(s) found. |
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1. Quantifying Spatial Variability Of Indigenous Nitrogen Supply For Precision Nitrogen Management In North China Plain... Y. Miao, Q. Cao, Z. Cui, F. Li, T.H. Dao, R. Khosla, X. Chen |
2. Sectioning And Assessment Remote Images For Precision Agriculture: The Case Of Orobanche Crenate In Pea CropThe software SARI® has been developed to implement precision agriculture strategies through remote sensing imagery. It is written in IDL® and works as an add-on of ENVI®. It has been designed to divide remotely sensed imagery into “micro-images”, each corresponding to a small area (“micro-plot”), and to determine the quantitative agronomic and/or environmental biotic (i.e. weeds, pathogens) and/or non-biotic (i.e. nutrient levels) indicator/s... L. Garcia-torres, D. Gomez-candon, J.J. Caballero-novella, M. Gomez-casero, J.M. Pe, M. Jurado-exp, F. Lopez-granados, I. Castillejo-gonz, A. Garc |
3. Developing An Active Crop Sensor-based In-season Nitrogen Management Strategy For Rice In Northeast ChinaCrop sensor-based in-season N management strategies have been successfully developed and evaluated for winter wheat around the world, but little has been reported for rice. The objective of this study was to develop an active crop sensor-based in-season N management strategy for upland rice in Northeast... Y. Yao, Y. Miao, S. Huang, M.L. Gnyp, R. Jiang, X. Chen, G. Bareth |
4. Management Of Remote Imagery For Precision AgricultureSatellite and airborne remotely sensed images cover large areas, which normally include dozens of agricultural plots. Agricultural operations such as sowing, fertilization, and pesticide applications are designed for the whole plot area, i.e. 5 to 20 ha, or through precision agriculture. This takes into account the spatial variability of biotic and of abiotic factors and uses diverse technologies to apply inputs at variable rates, fitted to the needs of each small defined area, i.e. 25 to 200... L. Garcia-torres, D. Gomez-candon, J.J. Caballero-novella, J.M. Pe, M. Jurado-exp, I. Castillejo-gonz, A. Garc, F. Lopez-granados, L. Prassack |
5. Algorithm to Estimate Sorghum Grain Number from Panicles Using Images Collected with a Smartphone at Field-scaleAn estimation of on-farm yield before harvest is important to assist farmers on deciding additional input use, time to harvest, and options for end uses of the harvestable product. However, obtaining a rapid assessment of on-farm yield can be challenging, even more for sorghum (Sorghum bicolor L.) crop due to the complexity for accounting for the grain number at field-scale. One alternative to reduce labor is to develop a rapid assessment method employing computer vision and artificial intelligence... G.N. Nocera santiago, P. Cisdeli magalhães, I. Ciampitti, L. Marziotte |
6. An Open Database of Crop Yield Response to Fertilizer Application for SenegalFood security is one of the major global challenges today. Africa is one of the continents with the largest gaps in terms of challenges for food security. In Senegal, about 60% of the population resides in rural areas and the cropping systems are characterized as a low productivity system, low input and in reduced areas, smallholder subsistence systems. Increasing crop productivity would have a positive impact on food security in this country. One of the main factors limiting crop productivity... F. Gomez, A. Carcedo, A. Diatta, L. Nagarajan, V. Prasad, Z. Stewart, S. Zingore, I. Ciampitti, P. Djighaly |
7. Exploring Crop Suitability in Senegal Across Global Warming Scenarios: an In-silico ApproachFood production systems in Africa are fragile and vulnerable to climate change. In this context, rising temperatures are the primary cause of the anticipated negative climate change impacts on crop yields. Future yield reductions poses a challenging setting for smallholders to attain self-sufficiency, but new opportunities for managing the risk via implementationof decisions towards mitigate these negative effects from an economic, nutritional, and productive standpoint. Therefore, the objectives... A. Carcedo, I. Ciampitti |
8. Using Remote Sensing to Quantify Biomass in AlfalfaSatellite images are a useful decision support tool to optimize management practices at on-farm scale. Based on this, the development of predictive tools to estimate pasture biomass can be a promising framework to determine the best cutting time, maximizing biomass without compromising yield parameters. Therefore, the main objective of this study was to develop a regression model that allows estimating a value of biomass to give as a recommendation to farmers. To collaborate in their decision... M.F. Lucero, A. Zajdband, C. Hernandez, I. Ciampitti, A. Carcedo |
9. Trends in Agricultural Technology Advancements: Insights from US Patent AnalysisMeeting the demand for food, fiber, and fuel production while addressing environmental concerns and enhancing societal benefits underscores the need to transition to conservation approaches and sustainable intensification pathways in current agricultural cropping systems. Technological advances in agriculture offer promising opportunities to facilitate this transition. Following this rationale, this study aims to analyze prevailing trends in agricultural technology advancements. Active patents... P.B. Cano, A. Carcedo, F. Gomez, C. Hernandez, V. Gimenez, I. Ciampitti |
10. Automated Analysis of Dairy Cow Bedding Behavior Patterns Based on Deep Learning TechnologyStalls are among the most frequently used facilities for dairy cows. Their design and use directly affect the cows’ health, comfort, and willingness to lie down. These factors are closely associated with both animal welfare and milk production. Therefore, monitoring stall use provides a practical basis for evaluating stall design and barn environment quality, in line with the Five Freedoms of Animal Welfare. This study proposes an automated system based on deep learning to analyze stall... C. Chang |
11. AI-driven Evapotranspiration Prediction Using Plant Wearable Sensor for Smart IrrigationThe global expansion of greenhouse cultivation has created a need for reliable crop evapotranspiration (ET) estimation to enable precise irrigation, thereby improving yields, enhancing crop quality, and addressing challenges related to water scarcity and environmental sustainability. This study proposes the development of a plant signal-based artificial intelligence (AI) model for ET prediction, tailored to the unique environmental conditions of greenhouse systems. Unlike empirical models that... W. Cho |
12. Development of an In-line Sc-ise Sensor System for Closed Hydroponic Nutrient MonitoringNutrient monitoring is crucial in closed hydroponic systems, where accurate control over individual ion concentrations directly influences crop yield and fertilizer efficiency. While electrical conductivity (EC) sensors are commonly used, they only measure total ionic strength and cannot distinguish between specific ions. Liquid-contact ion-selective electrodes (LC-ISEs) have been studied as an alternative but tend to suffer from durability issues and signal instability under the high-flow conditions... Y. Jang, W. Cho |