Proceedings
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| Filter results4 paper(s) found. |
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1. Development of a Label-free Electrochemical Biosensor for Detection of Infectious Hepatitis a VirusOne of the leading causes of foodborne viral illnesses in the world is Hepatitis A Virus, which is frequently involved in causing outbreaks linked to contaminated produce and shellfish (e.g., green onions and berries) due to contamination during cultivation, processing, or handling. HAV is highly stable in environment capable of remaining infectious on food matrices and in water for extended periods. Humans get infected primarily via the fecal-oral route... D. Kaur, R.P. Ramasamy, M. Esseili |
2. Precision Tillage Operations: Analyzing the Efficiency of Conventional and Robotic SystemsABSTRACT. The crop production sector is labor- and energy-intensive, significantly impacting the environment. Soil tillage is one of the most expensive and polluting technological operations; therefore, modern automated and precision technologies applied according to soil variability can help change economic costs and environmental pollution. This study evaluated the effects of site-specific variable depth tillage using two combinations of a conventional tractor and a multifunctional cultivator,... E. Šarauskis, S. Sokas, I. Bručienė, S. Buragienė, M. Kazlauskas, V. Naujokienė |
3. The Agronomic and Bioeconomic Aspects of Site-Specific Seeding Rates and Depths for Winter Wheat in LithuaniaPrecision seeding is one of the most important agrotechnological solutions for smart agriculture. It exploits the variability of soil properties in the field to increase the agronomic and economic efficiency of crops. This study investigated the impact of site-specific seeding (SSS) on the yield and productivity parameters of winter wheat in Lithuania, as well as its economic benefits, compared with conventional uniform rate seeding (URS). Experiments were conducted in a field divided into five... Z. Kriauciuniene, M. Kazlauskas, K. Romaneckas, S. Buragiene, I. Bručienė, E. Šarauskis |
4. A Machine Learning Framework for Crop Productivity Classification and Risk AssessmentThe integration of Artificial Intelligence and Remote Sensing is essential for the early identification of agricultural fields with suboptimal growing conditions. Such capabilities are vital for targeted interventions, supply chain logistics, and agricultural risk management. This study developed and validated a machine learning framework designed to classify the productivity conditions of corn, soybean, and wheat into ‘Low’, ‘Medium’, and ‘High’ tiers, utilizing... J.D. Xavier, K. Schenatto, G.V. Miranda, C.L. Bazzi, R. Sobjak |