Machine learning for the detection of soil pH, macronutrients, and micronutrients with crop and fertilizer recommendations
Year: 2024
Type: article
Abstract: The study aims to determine the levels of soil parameters such as soil pH, macronutrients, and micronutrients. After determining said parameters, the system appropriately recommends crops and fertilizers suitable for the soil samples. For soil pH and macronutrient levels, i.e., nitrogen, phosphorus, and potassium, these parameters can be detected using the soil test kit. Meanwhile, for soil micronutrients, i.e., copper, iron, and zinc, there is a need for the development of appropriate assays for colorimetric processes that can be done for the appropriate determination of said micronutrients. Comparison of available machine learning such as support vector machine algorithm, naïve Bayes algorithms, and K-nearest neighbor algorithm is a must to determine the well-fit algorithm that is considered fast and has high predictive power in classification and regression. The outputs of the colorimetric and spectrometric processes are the inputs in the machine learning activities intended for crop and fertilizer recommendation.
Authors: John Joshua Montañez, Jeffrey Sarmiento
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4404852512