1. Agricultural Advancements through Machine Learning Technologies.
- Author
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Sharma, Parul and Abrol, Pawanesh
- Abstract
Machine learning, a subset of artificial intelligence, is revolutionizing agriculture by enabling enhanced crop monitoring, disease detection, and yield prediction. Its application extends to precision farming, where it aids in optimizing irrigation, fertilization, and harvesting by analyzing large datasets from mobile phones, sensors and satellite images. It has played a crucial role in identifying and categorizing different types of plants. Specifically, Convolutional Neural Networks (CNN), a deep learning technology within ML, are employed to classify plant species based on images. In the present research work, we have employed machine learning for the classification of citrus species using Convolutional Neural Networks (CNNs). A dataset has been developed with images of fruits and leaves from ten citrus species. We have utilized transfer learning with architectures like Mobile Net, Alex Net, and Goog Le Net. The study demonstrates that combining multiple plant components in CNN analysis improves classification accuracy, with leaves providing more reliable results than fruits. This approach signifies a major advancement in agricultural technology, allowing for more precise and efficient farming practices. The findings indicate that expanding the dataset and incorporating more plant structures could further refine these models. This research highlights the potential of machine learning in agriculture, particularly in enhancing species classification, which is crucial for sustainable and productive farming. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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