Optimizing botanical farm crop variety selection: integration of machine learning mechanisms for green technology and sustainable solutions
Tóm tắt
Currently, the field of science and technology, particularly Artificial Intelligence (AI), is undergoing significant progress. AI involves the computer-based simulation of human cognitive functions. Within the realm of AI, machine learning, a specialized branch, utilizes mathematical algorithms to enhance computational capabilities. The incorporation of AI in agriculture offers opportunities to optimize the selection of viable plant species, leading to improved agricultural productivity, higher incomes for farmers, and overall economic development. By applying machine learning techniques to the "Agricultural Crop Dataset" the study has developed an effective system for predicting the most suitable plant species for farmers. This endeavor promotes the practical utilization of AI in agriculture, paving the way for sustainable economic growth.
Tài liệu tham khảo
V. Toan (2023). Application of Artificial Intelligence: The Key to Developing Modern and Sustainable Agriculture. Nhân Dân Online Newspaper. Source: https://nhandan.vn/ung-dung-tri-tue-nhan-tao-chia-khoa-phat-trien-nen-nong-nghiep-hien-dai-va-ben-vung-post744680.html.
L. Son (2023). Application of Artificial Intelligence Technology to Enhance Agricultural Productivity. Economic News Newspaper. Source: https://baotintuc.vn/kinh-te/ung-dung-cong-nghe-tri-tue-nhan-tao-de-nang-cao-nang-suat-nong-nghiep-20230323075602822.htm.
A.I.A. (2023). Agricultural crop dataset. Kaggle. Source: https://www.kaggle.com/datasets/agriinnovate/agricultural-crop-dataset
Patel, R. (2023). Crop yield prediction dataset. Kaggle. Source: https://www.kaggle.com/datasets/patelris/crop-yield-prediction-dataset
Mahajan, N. (2023). Crop production statistics India. Kaggle. Source: https://www.kaggle.com/datasets/nikhilmahajan29/crop-production-statistics-india
Powers, D. M. (2020). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061.
Ho, A. K. N. (2015). Méthodes de classifications dynamiques et incrémentales: application à la numérisation cognitive d'images de documents (Doctoral dissertation, Tours).
Klinkenberg, R. (2004). Learning drifting concepts: Example selection vs. example weighting. In Intelligent data analysis, 8(3), pp. 281-300.
Anh-Khôi, N. H., Hà-Duy-Nguyên, L., & Vĩnh-Khang, T. (2024, February). Artificial Intelligence Applied to Address Tourism Challenges: Predicting Hotel Room Cancellations. In 11th International Conference on Emerging Challenges: Smart Business and Digital Economy 2023 (ICECH 2023) (pp. 434-445). Atlantis Press.
Ngo, H. A. K., Pham, V. T., & Tran, V. T. (2024). Evolving with Klinkenberg’s Idea (EKI) Algorithms for Automatic Identification of Sa Huynh Antique Glass Artifacts. In 13th Conference on Information Technology and Its Applications 2024 (CITA 2024) (pp.81-92). Information And Communications Publishing House.
© 2023 DNTU. All rights reserved.
