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Precision Agriculture for Corn using Reinforcement Learning BY TAN CARLTONA REPORT SUBMITTED TO Universiti Tunku Abdul Rahman in partial fulfillment of the requirements for the degree of BACHELOR OF COMPUTER SCIENCE (HONOURS) Faculty of Information and Communication Technology (Kampar Campus)JAN 2024UNIVERSITI TUNKU ABDUL RAHMANREPORT STATUS DECLARATION FORMTitle:___Precision Agriculture for Corn using Reinforcement Learning___ ___ ___ Academic Session: __JAN 2024__I___TAN CARLTON___ (CAPITAL
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Identify the specific farm area for corn cultivation.
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Precision agriculture for corn is an approach that utilizes technology and data analysis to optimize field-level management regarding crop farming. It involves collecting and analyzing data from various sources such as soil sensors, satellite imagery, and weather stations to improve yield, manage resources efficiently, and reduce environmental impact.
Farmers and agricultural producers who are implementing precision agriculture practices for corn production are typically required to file any necessary documentation with local agricultural authorities or organizations, especially if they are receiving financial assistance or subsidies related to precision agriculture.
Filling out precision agriculture documentation for corn involves collecting data regarding soil health, crop yield, input usage (like fertilizers and pesticides), and environmental conditions. Farmers need to provide specific information based on their practices, including GPS data, field maps, and any technology used, in accordance with local regulations.
The purpose of precision agriculture for corn is to increase crop yield and efficiency, minimize environmental impact, and optimize resource usage by applying agricultural inputs precisely where and when they are needed, ultimately leading to sustainable and profitable farming practices.
Information that must be reported on precision agriculture for corn may include farm location, field data, crop input usage (such as seeds, fertilizers, herbicides), yield data, practices implemented, and any technological tools used for monitoring and managing the crops, as required by local agricultural regulations.
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