AGROWISE: Mobile Application for Agricultural Crop Recommendation Based on Machine Learning

• Ismael Vieira Guedes IFPI
• Felipe Gonçalves dos Santos IFPI
• Thiago Reis da Silva IFMA

DOI: https://doi.org/10.5753/erbase.2024.4416

Abstract

Agriculture faces challenges such as unpredictable climatic factors and crop diseases. The implementation of technological innovations, such as Machine Learning, has increased crop yield and quality. This branch of Artificial Intelligence is applied in precision agriculture, which uses large volumes of data to identify soil patterns, revolutionizing cultivation cycles. Integrating machine learning into agriculture can improve results and reduce risks. This paper proposes a mobile application for crop recommendation, using a machine learning model trained with data on temperature, humidity, pH, and soil information.

References

Cruz, F. Python: Escreva seus primeiros programas. Editora Casa do Código, 2015.

Dhankar, A.; Gupta, N. A systematic review of techniques, tools and applications of machine learning. In: IEEE Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks – ICICV, pp. 764-768, 2021.

Gaurav, A.; Chitra, L.; Munish, J. Data Analytics: Principles, Tools, and Practices: A Complete Guide for Advanced Data Analytics Using the Latest Trends, Tools, and Technologies (English Edition). BPB Publications, 2022.

Hamdan, M. F.; Noor, S. N. M.; Abd-Aziz, N.; Pua, T. L.; Tan, B. C. Green revolution to gene revolution: Technological advances in agriculture to feed the world. Plants, 11(10), 2022.

IBGE, Censo Agro 2017: população ocupada nos estabelecimentos agropecuários cai 8,8% | Agência de Notícias. Disponível em: [link]. Acesso em: 23 fev. 2024.

Ingle, Crop Recommendation Dataset. Disponível em: [link]. Acesso em: 28 fev. 2024.

Li, T.; Xia, T.; Wang, H.; Tu, Z.; Tarkoma, S.; Han, Z.; Hui, P. Smartphone app usage analysis: datasets, methods, and applications. IEEE Communications Surveys and Tutorials, 24(2), 937-966, 2022.

Moura, M. R. A.; Costa, L. S. F. Levantamento de artigos sobre interação humano-computador em periódicos de ciência, tecnologia e sociedade. In: Revista Tecnologia e Sociedad), v. 14, n. 33, 2018.

Narayana, P; Saxena, S. Online Crop Doctor using Machine Learning and Deep Learning. 2023.

ONU. Número de pessoas afetadas pela fome sobe para 828 milhões em 2021. Disponível em: [link]. Acesso em: 20 fev. 2024.

Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Duchesnay, E. Scikit-learn: Machine learning in python journal of machine learning research. Journal of machine learning research, 12, 2825-2830, 2011.

Prevelato, M. Sistema de recomendação de plantio utilizando aprendizado de máquina. 2023. 83 f. Trabalho de Conclusão de Curso (Engenharia de Controle e Automação) – Universidade Federal de Uberlândia, Uberlândia, 2023.

Raheem, M. A.; Hussain, M. S.; Ahmed, S. A. Machine Learning based Crop Recommendation on a Cloud. In IEEE 8th International Conference for Convergence in Technology (I2CT) (pp. 1-5), 2023.

Sri, B. S.; Pavani, G.; Sindhuja, B. Y. S.; Swapna, V.; Priyanka, P. L. An Improved Machine Learning based Crop Recommendation System. In IEEE International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) (pp. 64-68), 2023.

Shah, S.; Jain, N.; Shah, S.; Bide, P. J. A Flutter Application For Farmers. In IEEE Asian Conference on Innovation in Technology (ASIANCON) (pp. 1-8), 2021.

The World Bank. Agriculture and Food. Disponível em: [link]. Acesso em: 10 jan. 2024.

Van Klompenburg, T.; Kassahun, A.; Catal, C. Crop yield prediction using machine learning: A systematic literature review. Computers and electronics in agriculture, 177, 105709, 2020.