Soil and Crop Production Analyzer: Advancing Agricultural Revolution through Random Forest Algorithm
DOI:
https://doi.org/10.54536/ajsts.v5i1.7318Keywords:
Agricultural Technology, Crop Recommendation System, General Santos City, IoT Sensors, Machine Learning, Precision Agriculture, Random Forest Algorithm, Soil Fertility Classification, Soil Nutrient Analysis, Sustainable FarmingAbstract
This study responds to the challenge faced by many farmers in accessing reliable methods of soil testing to ascertain nutrient levels in the soil and recommend the crops that best suit the area for planting. Guesswork and experience make farmers in most rural areas depend on long waiting for laboratory results, as well as traveling to distant testing facilities, which mostly yield poor results. This study, therefore, tries to solve this problem by suggesting a portable Soil and Crop Production Analyzer that uses accessible information on soil nutrients and machine learning techniques to offer recommendations in real-time. The system integrates WiFi-enabled IoT sensors with a mobile application in measuring the following key parameters in the soil: Nitrogen (N), Phosphorus (P), Potassium (K), and pH levels. The Random Forest algorithm classifies the fertility level of the soil and recommends suitable crops from the measured parameters that were used to process the gathered data on soil. Historical data tracking, offline capability, and a farmer-friendly interface are some additional features of the mobile application to ensure usability even at the remotest areas. By making soil testing and crop recommendations more accessible, the system encourages better farming practices and enhances agricultural productivity. The present study is targeting specifically farmers in General Santos City to gain access to more resilient and data-based agricultural communities as a consequence of its implementation.
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Copyright (c) 2026 Ivan Klenn P. Ticaro, Agaspar Adrian S. Latayada, Charisse S. Ronquillo, MIT, Abejah S. Paculdo, MIT
This work is licensed under a Creative Commons Attribution 4.0 International License.
