Tomato disease and pest diagnosis method based on the Stacking of prescription data

[Display omitted] •Prescription data was first used to diagnose tomato pests and diseases.•A tomato pests and diseases diagnose model was build based on feature selection and Stacking ensemblelearning.•The 37 extracted features can achieve the nearly same diagnosis performance as the 50 original features.•Stacking models exhibits a slightly superior performance compared to the best model (LGBM) among ten diagnosis models.•The minimum and optimal base classifiers are chosen for the stacking model. Crop prescription data contains an extensive amount of information on crops, environment and pests, and has notable diagnostic capabilities. At present, there is lack of feasible methods for efficiently mining crop prescription data to perform accurate diagnoses. In view of the above problems, the purpose of our study is to mine prescription data information and assist the accurate diagnosis of crop diseases. In this paper, six tomato diseases and pests, namely, the tomato virus disease, tomato late blight, tomato gray mold, aphid, thrips and whiteflies, were explored to construct a diagnosis model based on prescription data mining. Original prescription data was subjected to pre-processing, text labeling and one-hot coding. The recursive feature elimination (RFE) method was then employed to extract 37 key features relating to crop diseases and pests from original 50 features. We constructed a tomato disease and pest diagnosis model based on two-stage Stacking ensemble learning to improve the diagnosis accuracy. The experimental results demonstrated the proposed diagnosis model in this paper exhibits a slightly superior performance compared to the best model (LGBM) among ten diagnosis models. The optimal Stacking model is composed of two layers: base-classifiers including GDBT, XGBoost and LGBM, and meta-classifier RF. The diagnosis accuracy of the proposed model for the tomato virus disease reached 94.84%, with an F1-score of 95.98% and overall accuracy of 80.36%. It also performed well on the multi-classification metrics: Macro avg (Precision: 76.55%, Recall: 78.17%, F1-score: 77.05%) and Weighted avg (Precision: 80.96%, Recall: 80.36%, F1-score: 80.50%). Moreover, following feature selection, the Stacking-based diagnosis model can reduce the running time by 12.08% with unchanged diagnosis accuracy. The proposed diagnosis model meets the real-world diagnosis requirements. This work provides new research concepts and a methodological foundation for future crop di