AgroScan : Crop Identification Application using Artificial Intelligence Approach
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Date
2024
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Nanotechnology Perceptions
Abstract
Artificial intelligence (AI) has the potential to revolutionize agriculture, particularly in the realm of crop analysis. AI-based systems can accurately identify crops in images, even under challenging conditions such as low light or shading. These systems have numerous applications, including crop monitoring and management, precision agriculture, and food safety and security. The goal of this research paper is to develop a robust, accurate, and efficient AI-based crop identification system. The system will be trained on an extensive dataset of crop images to identify various crops and detect certain diseases in new images. To evaluate our system's performance, we will compare it against new and emerging methods using a held-out test set. This study aims to develop an AI-driven crop identification system that will benefit farmers and other stakeholders in the agricultural sector by improving crop yields and quality, reducing costs, minimizing environmental impacts, and enhancing food safety.
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uGDX
Keywords
Artificial Intelligence, Computer Vision, Convolutional Neural Networks, Crop Identification, Precision Agriculture, Agricultural Technology, Crop Disease Detection, Image Processing, Machine Learning in Agriculture