“Green Thumb” – Smart AI Assistant for Crop Recommendation and Plant Care
Main Article Content
Abstract
With the growing need for better farming and gardening practices, agriculture and gardening depend more on smart and informed decisions. But many farmers and gardeners still depend on traditional methods which do not always give accurate results. Because of this, it can lead to reduce productivity and sometimes poor plant growth. This paper presents the design and development of GreenThumb, a system that help users to take better decisions in crop selection and plant care. It is a web-based platform which uses open-source Artificial Intelligence to give crop recommendations based on soil type and weather condition. Along with this, it also helps users in identifying plant problems using symptom-based inputs and suggest possible solutions. A basic evaluation of the system shows that it is easy to use and gives useful suggestions for daily gardening needs. The overall approach mainly focuses on making technology simple and accessible, especially for people who have less technical knowledge, and it also promote better and more sustainable farming practices.
Article Details
Section
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.
References
[1] A. L. Samuel, “Some studies in machine learning using the game of checkers,” IBM Journal of Research and Development, vol. 3, no. 3, pp. 210–229, 1959.
[2] T. O. Ayodele, “Types of machine learning algorithms,” New Advances in Machine Learning, vol. 3, pp. 19–48, 2010.
[3] H. Wang, Z. Lei, X. Zhang, B. Zhou, and J. Peng, “Machine learning basics,” in Deep Learning, 2016, pp. 98–164.
[4] G. Bonaccorso, Machine Learning Algorithms. Packt Publishing Ltd., 2017.
[5] K. Gurney, An Introduction to Neural Networks. CRC Press, 1997.
[6] S. Sharma, and A. Athaiya, “Activation functions in neural networks,” Towards Data Science, vol. 6, no. 12, pp. 310–316, 2017.
[7] S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” in 2017 International Conference on Engineering and Technology (ICET), 2017, pp. 1–6.
[8] M. A. Hearst et al., “Support vector machines,” IEEE Intelligent Systems, vol. 13, no. 4, pp. 18–28, 1998.
[9] I. Steinwart and A. Christmann, Support Vector Machines. Springer, 2008.
[10] S. Chen et al., “A novel selective naïve Bayes algorithm,” Knowledge-Based Systems, vol. 192, p. 105361, 2020.
[11] K. Taunk et al., “A brief review of nearest neighbor algorithm,” in ICCS, 2019, pp. 1255–1260.
[12] B. Charbuty and A. Abdulazeez, “Decision tree algorithm for machine learning,” Journal of Applied Science and Technology Trends, vol. 2, no. 1, pp. 20–28, 2021.
[13] M. R. Segal, “Machine learning benchmarks and random forest regression,” 2004.
[14] D. W. Hosmer Jr et al., Applied Logistic Regression. John Wiley & Sons, 2013.
[15] R. Ghadge, J. Kulkarni, P. More, S. Nene, and R. Priya, “Prediction of crop yield using machine learning,” Int. Res. J. Eng. Technol.(IRJET), vol. 5, 2018.
[16] N. H. Kulkarni, G. N. Srinivasan, B. M. Sagar, and N. K. Cauvery, “Improving crop productivity through a crop recommendation system using ensembling technique,” in 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS), 2018, pp. 114–119.
[17] S. Pudumalar, E. Ramanujam, R. H. Rajashree, C. Kavya, T. Kiruthika, and J. Nisha, “Crop recommendation system for precision agriculture,” in 2016 Eighth International Conference on Advanced Computing (ICoAC), 2017, pp. 32–36.
[18] K. G. Liakos, P. Busato, D. Moshou, S. Pearson, and D. Bochtis, “Machine learning in agriculture: A review,” Sensors, vol. 18, no. 8, p.2674, 2018. [Online]. Available: https://www.mdpi.com/1424-8220/18/ 8/2674