Proceedings
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| Filter results4 paper(s) found. |
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1. Enhancing Farmers' Indigenous Knowledge Management in Cassava Varietal Trial Using Agro Ecosystem Analysis, Farmers' Drama Group and Animations in Eastern part of Nigeria.Researchers continue to come up with new varieties but farmer perspectives and preferences are very important factors for new varieties to spread in farmers’ communities. Researcher priorities alone are not enough. A variety may be ‘scientifically perfect... C.C. Asiabaka, M.O. Adesope, C.C. Ifeanyi- obi, R.N. Nwakwasi, F. Nnadi, E.C. Matthews- njoku, J. Chikaire |
2. Toward a Precision Agricultural Implementation for Sugar Cane Plantations in Southwestern Region of Colombia, South AmericaThe Colombian Sugar Cane Research Center, CENICAÑA, has initiated an ambitious project for the implementation of Precision Agriculture (PA) technologies in the Cauca river valley region, where one of its main objectives is to have the ability to collect large volumes of geospatial data. The main sugarcane growers in the country perform their work in the selected work area, which covers an area of approximately 242,000 ha, characterized by diverse topographic and edaphic conditions.... J.A. Celades, J.H. Caicedo, C.E. García, H. Mora |
3. AI for Genomic Agriculture — from Sequence to Field ImpactGenomics offers powerful opportunities to enhance crop yield, resilience, and nutritional value, yet the complexity and scale of genomic, transcriptomic, and epigenomic data pose significant challenges for interpretation and application. Artificial intelligence (AI), particularly machine learning and deep learning, provides powerful approaches to decode this complexity and accelerate precision agriculture. I will present AI-based methods developed in my laboratory for annotating plant... C. Chen |
4. AI for Genomic Agriculture — from Sequence to Field ImpactGenomics offers powerful opportunities to enhance crop yield, resilience, and nutritional value, yet the complexity and scale of genomic, transcriptomic, and epigenomic data pose significant challenges for interpretation and application. Artificial intelligence (AI), particularly machine learning and deep learning, provides powerful approaches to decode this complexity and accelerate precision agriculture. I will present AI-based methods developed in my laboratory for annotating plant... C. Chen |