Modernizing soil laboratory data workflows: a web-based approach with statistical and machine learning integration

Abstract

Data management is a crucial component of research and laboratory work. Soil analysis is a fundamental practice in modern agriculture, environmental science, and land management, yielding a wealth of data. Maintaining the integrity of this data is pivotal for precision agriculture, influencing evidence-based decisions related to crop yield optimization, nutrient management, and sustainable land use. However, the current data management relies on small-scale data management software, such as Microsoft Excel and Microsoft Access. It involves the manual creation of records, among other approaches, for managing soil test data. These, however, encourage scattered data storage, are limited to storing small amounts of data, and make data analysis and visualization difficult, as well as error-prone due to the absence of validation and a standardized arrangement of stored data. The high access latency associated with these methods further delays data access, thus compromising efficient and timely data management efforts. Completely relying on these for soil-test data management is like bringing a knife to a gunfight, as the large volumes of data generated over time render them inefficient. To overcome these challenges, a soil data management system was built using Django to ensure scalability and security. The system has centralized soil test data storage, automated calculations and incorporated Machine Learning powered predictions to enhance data accuracy and streamlined reporting, accessibility, and usability with integrated user-friendly interface, standardized reporting, and mobile compatibility. The improved data accessibility, accuracy and timely reporting. The system evaluation with data of soil properties could categorize fertility into low moderate and high using regression trees with an accuracy of 99.6% thus being a reliable system to aid decision making on soil fertility management This innovative approach, will help in transforming soil test data management, providing a robust tool for researchers, agronomists, and policymakers to make informed decisions on soil management.