Weather-Based Yield Prediction
Accurate weather-based yield prediction is essential for modern agriculture. Using advanced technologies like remote sensing, AI, and big data analytics, farmers can forecast crop performance based on climatic variables such as temperature, rainfall, and humidity. These predictions help in planning irrigation, pest control, and harvest schedules, reducing losses and maximizing output. Weather-based models integrate historical data and real-time monitoring to provide localized insights. For instance, drought-prone areas can adjust planting dates or switch to resilient crop varieties based on predictions. Governments and agribusinesses are investing in these tools to support food security and climate adaptation. Such innovations ensure informed decision-making, empowering farmers to tackle uncertainties effectively.
Dachang Zhang
Water & Eco Crisis Foundation, United States
Srinivasa Rao Mentreddy
Alabama A&M University, United States
Usha R Palaniswamy
Maria College, United States
Dilip Nandwani
Tennessee State University, United States
Stephen Jennewein
University of Florida, United States
Fabio Gresta
University of Messina, Italy
Nedyalka Valcheva
Vocational High School, BulgariaSubmit your abstract Today
Title : Climate change greenhouse gas (CO2) impact – agriculture crop production: Quality improvement
Madhusudan H Fulekar, Research & Development Cell, Parul University, India
Title : Teaching food and nutrition security as a critical concern in higher education capstone
Usha R Palaniswamy, Maria College, United States
Title : Current status of precision agriculture in the USA
Kasiviswanathan Muthukumarappan, South Dakota State University, United States
Title : Advances in Indian agriculture and horticulture
V P S Arora, Kumaun University, India
Title : Markers of PM produced by biomass combustion and development of a sampling and analysis technique
Enrico Paris , CREA-IT , Italy
Title : Agroecological practices and their effects on ecosystem services in sustainable mediterranean cropping systems
Fabio Gresta, University of Messina, Italy