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DTEND:20230304T120000Z
UID:81f3a4774f0e365d06e1615070dfba90-425
DTSTAMP:19700101T120015Z
DESCRIPTION:Towards Sustainable Agriculture prioritizing Global South using Machine Learning
URL;VALUE=URI:https://www.csa.iisc.ac.in/newweb/event/425/towards-sustainable-agriculture-prioritizing-global-south-using-machine-learning/
SUMMARY:The agricultural sector, which is a major source of employment in the global south, is unfortunately the second largest contributor to greenhouse gas emissions globally after energy. Farmers are vulnerable to climate-related problems such as droughts, floods, and crop failure due to extreme temperatures. To mitigate these problems, it is crucial to develop innovative technological solutions that cater to the specific climate and socio-economic needs of the agricultural sector, thereby enabling it to advance rapidly in developing countries. Identifying different land features, such as fields, trees, and dug wells, and analyzing them to optimize water consumption, crop yields, and soil carbon sequestration is essential. Therefore, we concentrate on three areas: Agricultural Landscape Understanding (ALU) for automatic identification of land features, Agriculture Monitoring and Event Detection (AMED) for automatic crop monitoring, and Soil Carbon Sequestration for a deeper understanding of the soil organic carbon change.
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