Headcount of Sunflowers in a Sunflower Crop for Accurate Yield Estimation
Agricultural productivity and sustainability are critical challenges facing the world, particularly in countries like Pakistan, the economy heavily relies on agriculture. The production of oilseeds, such as sunflower, is crucial to meet the domestic demand for edible oil. However, low crop yields hinder the industry's growth, highlighting the need for reliable solutions. The proposed system overcomes the limitations of traditional crop monitoring approaches, such as manual counting, by using machine learning algorithms to detect sunflower heads in images. This system provides a user-friendly world map interface that displays the mosaics of sunflower fields on different dates. This map enables farmers to visually locate their sunflower fields and track the progress of their crops, providing them with valuable insights to make informed decisions. The web application allows for regular crop monitoring and estimation of crop yield over time, providing farmers with valuable insights for optimizing their production. This project demonstrates the potential of smart farming and precision agriculture to address global food security and sustainability challenges.
Faculty
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Dr. Zuhair ZafarDr. Zuhair Zafar
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Dr. Muhammad Moazam FrazDr. Muhammad Moazam Fraz
Students
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Muhammad Ali (BSCS-9)
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Momin Anjum (BSCS-9)
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Amal Saqib (BSCS-9)