In this paper, we present an optimized Machine Learning (ML) algorithm for predicting land suitability for crop (sorghum) production, given soil properties information. We set-up experiments using Parallel Random Forest (PRF), Linear Regression (LR), Linear Discriminant Analysis (LDA), KNN, Gaussian Naïve Bayesian (GNB) and Support Vector Machine (SVM). Experiments were evaluated using 10 cross fold validation. We observed that, parallel random forest had a better accuracy of 0.96 and time of execution of 1.7 sec. Agriculture is the main stream of food security. Kenya relies on agriculture to feed its population. Land evaluation gives potential of land use, in this case for crop production. In the Department of Soil Survey in Kenya Agriculture and Livestock Research Organization (KALRO) and other soil research organizations, land evaluation is done manually, is stressful, takes a long time and is prone to human errors. This research outcomes can save time and improve accuracy in land evaluation process. We can also be able to predict land suitability for crop production from soil properties information without intervention of a soil scientist expert. Therefore, agricultural stakeholders will be able to efficiently make informed decisions for optimal crop production and soil management.
Autores y editores
Kennedy Senagi, Nicolas Jouandeau, Peter Kamoni
The Dedan Kimathi University of Technology motto is: “Better Life through Technology”.
To be a Premier Technological University Excelling in Quality Education, Research, and Technology Transfer for National Development.
Proveedor de datos
Land Development and Governance Institute
MISSION: To contribute to improved livelihoods through offering a bridge between communities, stakeholders and policy makers in the promotion of equitable access and sustainable management of land and natural resources.