Scopus Index Publications

Now showing 1 - 2 of 2

Open Access
A novel application with explainable machine learning (SHAP and LIME) to predict soil N, P, and K nutrient content in cabbage cultivation
(Elsevier B.V., 2025-08) Abekoon, T; Sajindra, H; Rathnayake, N; Ekanayake, I.U.; Jayakody, A; Rathnayake, U
Cabbage (Brassica oleracea var. capitata) is commonly cultivated in high altitudes and features dense, tightly packed leaves. The Green Coronet variety is well-known for its robust growth and culinary versatility. Maximizing yield is crucial for food sustainability. It is essential to predict the soil's major nutrients (nitrogen, phosphorus, and potassium) to maximize the yield. Artificial intelligence is widely used for non-linear predictions with explainability. This research assessed the predictive capabilities of soil nitrogen, phosphorus, and potassium levels with explainable machine learning methods over an 85-day cabbage growth period. Experiments were conducted on cabbage plants grown in central hills of Sri Lanka. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were used to clarify the model's predictions. SHAP analysis showed that high feature values of the number of days and plant average leaf area negatively impacted for nutrient predictions, while high feature values of leaf count and plant height had a positive effect on the nutrient predictions. To validate the results, 15 greenhouse-grown cabbage plants at various growth stages were selected. The nitrogen, phosphorus, and potassium levels were measured and compared with the predicted values. These insights help refine predictive models and optimize agricultural practices. A user-friendly application was developed to improve the accessibility and interpretation of predictions. This tool is a user-friendly platform for end-users, enabling effective use of the model's predictive capabilities.
Open Access
A novel deep learning model to predict the soil nutrient levels (N, P, and K) in cabbage cultivation
(Elsevier, 2024-03) Sajindra, H; Abekoon, T; Jayakody, J.A.D.C.A.; Rathnayake, U
Cabbage (Brassica oleracea) is a green cruciferous vegetable. Major nutrients (nitrogen, phosphorus, and potassium) are frequently applied to the soil due to low fertility levels. However, optimizing required fertilizer levels are extremely important to avoid any overuse and underuse. Therefore, it is important to develop a comprehensive methodology for evaluating the major nutrients in the soil. In this research, a deep learning model was introduced to predict the nitrogen, phosphorus, and potassium content of the soil by analyzing the growing characteristics of the plants, such as plant height, the number of leaves, and the average leaf area of the plant. To achieve this, the growing characteristics of the cabbage plants were recorded weekly along with the respective soil nitrogen, phosphorus, and potassium content of the nearby soil. After the data was trained using the Levenberg–Marquardt algorithm and tested with different transfer functions such as logarithmic sigmoid, pure linear, and tangent sigmoid, better predictions were obtained through the model. According to the Pearson correlation values, pure linear and tangent sigmoid showed higher values, ranging from 0.99 for training, testing, validation, and all data points from the model, indicating a strong relationship between the actual and predicted values. According to the Mean Square Error values, the tangent sigmoid transfer function outperformed the others, giving a value of 1.0813, indicating better predictions of the soil nitrogen, phosphorus, and potassium content from the model

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