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UREA: A Case Study on Identifying Organic Fertilizer, Nutrients based on Color Characteristics Using Random Forest Algorithm for Small-scale Vegetable Farms
Subject area: Agriculture and Veterinary Sciences · Area of research: Information Technology
Abstract
Effective fertilizer management is critical to the productivity and sustainability of small-scale vegetable farms. However, identifying different types of fertilizers, especially organic and inorganic fertilizers, can only be possible with specialized tools. Using the Random Forest algorithm, this case study presents a new method to identify nutrients from organic fertilizers such as urea based on their color characteristics. We created a machine-learning show by collecting color-based information from different fertilizer tests. This show can precisely classify fertilizers based on their visual properties. This investigation points to supplying smallholder agriculturists with a cost-effective and open solution that permits them to form educated choices of supplements utilized without requiring costly research facility gear. Our conclusion illustrates that the Sporadic Forest calculation can accurately recognize urea and other normal fertilizers when arranged with a comprehensive dataset of color characteristics. This approach might make strides in fertilizer organization on small-scale vegetable farms, diminishing the danger of mishandling and making more viable agrarian sharpens. The study also recommends that the process be further analyzed for enhanced reliability and flexibility and suggestions on practical implementation in small-scale agricultural production. This research has added value to the field of precision agriculture, providing innovative solutions for resource-constrained rural environments by using machine learning.
Keywords
Organic Fertilizer
How to cite this paper
@article{1705902,
author = {Cruz, Jershwin Marc A., Fuentes, Justine, Bautista, Mark John B., Pagulayan, Eunice Vielle L.; Tabor, Dennis Justine, Sheinn Reyes; Johani Basaula},
title = {UREA: A Case Study on Identifying Organic Fertilizer, Nutrients based on Color Characteristics Using Random Forest Algorithm for Small-scale Vegetable Farms},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {7},
number = {12},
pages = {134-143},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1705902.pdf},
abstract = {Effective fertilizer management is critical to the productivity and sustainability of small-scale vegetable farms. However, identifying different types of fertilizers, especially organic and inorganic fertilizers, can only be possible with specialized tools. Using the Random Forest algorithm, this case study presents a new method to identify nutrients from organic fertilizers such as urea based on their color characteristics. We created a machine-learning show by collecting color-based information from different fertilizer tests. This show can precisely classify fertilizers based on their visual properties. This investigation points to supplying smallholder agriculturists with a cost-effective and open solution that permits them to form educated choices of supplements utilized without requiring costly research facility gear. Our conclusion illustrates that the Sporadic Forest calculation can accurately recognize urea and other normal fertilizers when arranged with a comprehensive dataset of color characteristics. This approach might make strides in fertilizer organization on small-scale vegetable farms, diminishing the danger of mishandling and making more viable agrarian sharpens. The study also recommends that the process be further analyzed for enhanced reliability and flexibility and suggestions on practical implementation in small-scale agricultural production. This research has added value to the field of precision agriculture, providing innovative solutions for resource-constrained rural environments by using machine learning.},
keywords = {Organic Fertilizer},
month = {June},
}