Ul Haq Yasin, Shahbaz Muhammad, Asif Hm Shahzad, Al-Laith Ali, Alsabban Wesam, Aziz Muhammad Haris
Department of Computer Science and Engineering, University of Engineering and Technology Lahore Narowal Campus, Narowal, Pakistan.
Department of Computer Engineering, University of Engineering and Technology Lahore, Lahore, Punjab, Pakistan.
PeerJ Comput Sci. 2022 Oct 3;8:e1109. doi: 10.7717/peerj-cs.1109. eCollection 2022.
Soil study plays a significant role in the cultivation of crops. To increase the productivity of any crop, one must know the soil type and properties of that soil. The conventional soil type identification, grid sampling and hydrometer method require expert intervention, more time and extensive laboratory experimentation. Digital soil mapping, while applying remote sensing, offers soil type information and has rapidity, low cost, and spatial resolution advantages. This study proposes a model to identify the soil type using remote sensing data. Spectral data of the Upper Indus Plain of Pakistan Pothwar region and Doabs were acquired using fifteen Landsat eight images dated between June 2020 to August 2020. Bare soil images were obtained to identify the soil type classes Silt Loam, Loam, Sandy Loam, Silty Clay Loam and Clay Loam. Spectral data of band values, reflectance band values, corrective reflectance band values and vegetation indices are practiced studying the reflectance factor of soil type. Regarding multi-class classification, Random Forest and Support Vector Machine are two popular techniques used in the research community. In the present work, we used these two techniques aided with Logistic Model Tree with 10-fold cross-validation. The classification with the best performance is achieved using the spectral data, with an overall accuracy of 86.61% and 84.41% for the Random Forest and Logistic Model Tree classification, respectively. These results may be applied for crop cultivation in specific areas and assist decision-makers in better agricultural planning.
土壤研究在作物种植中起着重要作用。为了提高任何作物的产量，必须了解土壤类型及其特性。传统的土壤类型识别、网格采样和比重计法需要专家干预、更多时间和广泛的实验室实验。数字土壤制图在应用遥感技术时，能提供土壤类型信息，具有快速、低成本和空间分辨率高的优势。本研究提出了一种利用遥感数据识别土壤类型的模型。使用了2020年6月至2020年8月期间的15幅陆地卫星8号图像，获取了巴基斯坦波托哈尔地区印度河上游平原和河间地的光谱数据。获取裸土图像以识别粉砂壤土、壤土、砂壤土、粉质粘壤土和粘壤土等土壤类型类别。通过研究土壤类型的反射因子，实践了波段值光谱数据、反射率波段值、校正反射率波段值和植被指数。关于多类分类，随机森林和支持向量机是研究界常用的两种技术。在本研究中，我们使用这两种技术，并辅以具有10折交叉验证的逻辑模型树。使用光谱数据实现了最佳性能的分类，随机森林分类和逻辑模型树分类的总体准确率分别为86.61%和84.41%。这些结果可应用于特定地区的作物种植，并协助决策者进行更好的农业规划。
