Soil Condition Monitoring for organic tea plantation by using machine learning

Abstract

In tea farming, it is important to monitor soil conditions so as to maintain good health of the soil and a high-quality tea. In this research, machine learning techniques are employed to optimize soils management practices in these plantations. For instance, various parameters including pH, potassium, calcium and magnesium contents were tested on loamy soils obtained from different areas. Data has been pre-processed and used for training different machine models like Random Forest, Gaussian Naïve Bayes, Decision Tree, Support Vector Classifier (SVC), Multi-Layer Perceptron (MLP), among others. Among the above-mentioned ones with a highest accuracy rate of 99.18% Random Forest was able to predict accurately the soil condition having effect on management of the same. This finding will be important to farmers and agricultural scientists who would like to enhance organic farming with the help of data analysis. In this study, these models had a very high predictive capability when it came to soil status. It indicated that Random Forest was the most accurate with 99.18% accuracy implying that it is good at analyzing soil data and providing reliable predictions for the same. These were closely followed by Gaussian Naive Bayes, Decision Tree, SVC and MLP which were other models that also performed well with accuracies of 98.36%, 98.63%, 98.77% and 97.95% respectively.