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Deep learning in Smart Agriculture Abstract The abstract highlights the widespread application of Deep Learning (DL) in agriculture, specifically mentioning Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Generative Adversarial Networks (GAN). It emphasizes the need for researchers in agriculture to understand these DL algorithms to enhance data analysis and research outcomes in the field. The article ai.. 2024. 4. 12.
Global Scientific Basis for Action: CSA Abstract The concept of Climate-Smart Agriculture (CSA) aims to tackle the challenges posed by climate change and limited agricultural expansion by focusing on enhancing productivity, resilience, and sustainability in food production. It seeks to contribute to economic development, poverty reduction, and food security while minimizing trade-offs. CSA involves interdisciplinary research and scien.. 2024. 4. 11.
Transition to Agriculture to Landscapes in Climate Smart Tech Abstract The text discusses the concept of transitioning agricultural systems into "climate-smart landscapes" to achieve objectives like improved food security and climate change adaptation. Key features of such landscapes include implementing climate-smart practices at the field level, ensuring diverse land use for resilience, and managing land use interactions at a landscape scale. To successf.. 2024. 4. 10.
Integrated Food-Energy System with CSA Abstract The abstract discusses the need for increased food production to feed the growing population by 2050, highlighting the challenges related to energy use in agriculture and the impact on the environment. It emphasizes the importance of sustainable practices like agroforestry and integrated food-energy systems to mitigate these challenges and provide food and energy to both rural and urban.. 2024. 4. 9.

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