Prediction of Soil Quality in Rwanda for Ideal Cultivation of Potato (Solanum tuberosum) Using Fuzzy Logic and Machine Learning

The ability to estimate soil quality has great value for agriculture, especially for low-income regions with minimal agricultural and financial resources. This prediction provides users with information that is useful in determining whether the soil is suitable for a specific crop, such as potato (Solanum tuberosum). Farmers in Rwanda lack information on soil quality. There are not enough soil laboratories to perform the requisite measurements of NPK, pH, and organic carbon, nor are there enough experts to analyze the data and provide farmers with timely results. The prime objective of the proposed study is to develop a predictive framework that can estimate soil quality for the ideal cultivation of potato (Solanum tuberosum) considering a case study of Rwanda. In this study, bootstrapping is used to augment the small soil dataset, and fuzzy logic is used to label soil data into four classes of soil suitability, with verification of the labeling by soil experts. Several machine learning methods are then tested on the labeled data, resulting in the classification of suitability for the augmented dataset and an assessment of their performance as a way to support experts in predicting soil quality. All machine learning methods applied were viable, with the best performance achieved using an artificial neural network. The quantified outcome showed that the adoption of a neural-network-based scheme has an average accuracy of 32% in contrast to other learning schemes. However, 70%-80% accuracy was achieved upon the adoption of fuzzy logic.