Efficient Crop vs Weed Detection in Precision Agriculture: A CNN Approach for Real-Time Decision Making | IEEE Conference Publication | IEEE Xplore
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Efficient Crop vs Weed Detection in Precision Agriculture: A CNN Approach for Real-Time Decision Making


Abstract:

Crop vs weed detection plays an important approach in the present day scenario, as it is more useful for the farmers as well as for cultivating healthier crops. Weed grow...Show More

Abstract:

Crop vs weed detection plays an important approach in the present day scenario, as it is more useful for the farmers as well as for cultivating healthier crops. Weed growth in between the crops reduces crop productivity and degrades agricultural land. There are many ways to control the growth of weeds such as herbicides spraying manually, etc. The earlier used techniques are not that much efficient in finding or eliminating the weeds. Such methods have a no of drawbacks, including increased time consumption and the potential for herbicide spraying to damage actual crops. As many methods are already used to solve this problem, we are introducing cutting-edge Convolutional Neural Network (CNN) architecture which strikes a harmonious balance between computational efficiency and model accuracy, using CNN helps in taking real-time decision-making and resource allocation in the field. The evaluation of our proposed CNN model is conducted on the available dataset, which helps in identifying and distinguishing between crops and weeds. The method that we are applying will contribute to the advancement of precision agriculture by giving an efficient computational solution, which is more helpful for the farmers and can be applied in the real-world farming scenario and agricultural land.
Date of Conference: 01-03 March 2024
Date Added to IEEE Xplore: 06 May 2024
ISBN Information:
Conference Location: Bangalore, India

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