A novel feature based algorithm for soil type classification

• Published in: Complex & intelligent systems Vol. 8; no. 4; pp. 3377 - 3393
• Main Authors: Uddin, Machbah, Hassan, Md. Rakib
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2022; Springer Nature B.V
• Subjects: Algorithms; Classification; Complexity; Computational Intelligence; Crop production; Data Structures and Information Theory; Engineering; Histograms; Image analysis; Image classification; Machine learning; Original Article; Performance evaluation; Soil chemistry; Soil classification; Soils; Bangladesh; Haralick feature; Soil classification; Automated soil analysis; Quartile HOG feature; Pixels feature; Machine learning
• ISSN: 2199-4536; 2198-6053; 2198-6053
• DOI: 10.1007/s40747-022-00682-0

Agriculture is the backbone of Bangladesh’s economy and it is one of the largest employment sectors. In Bangladesh, the population is increasing rapidly and at the same time, the total cultivable land is decreasing significantly. To ensure maximum crop production using the limited land resources, it is essential to identify and select the appropriate type of soil because different crops need different soil types. Currently, there are two types of methods available to determine the soil type, namely chemical and image analysis. Although the first one is accurate, it is expensive and time consuming. On the other hand, image based soil classification is much cheaper and faster but its accuracy level is low. In this study, we present a novel feature based algorithm that combines quartile histogram oriented gradients (Q-HOG), most frequent φ -Pixels and a new feature selection method for classifying soil types. We have used four machine learning algorithms and evaluated the performance with different sets of features. We have also compared our work with two prominent and recent works on image-based soil classification systems. The experimental results show that the performance of our proposed method in terms of four standard evaluation metrics, namely accuracy, precision, F1_score, and recall scores are higher than the existing image-based soil classification systems.