article · Potato Research
Early and late blight pose significant threats to potato crops by lowering both yield and quality. By evaluating environmental conditions such as temperature, humidity, wind speed, and atmospheric pressure across more than 4,000 weather records, predictive systems can anticipate disease prevalence. Complex data relationships were examined using analytical techniques including K-means clustering, principal component analysis, and copula analysis. Several machine learning models, including logistic regression, gradient boosting, support vector machines, K-nearest neighbours, and multilayer perceptron networks, were assessed with and without feature selection methods like binary Greylag Goose Optimisation. The multilayer perceptron model paired with feature selection delivered the strongest performance, achieving an accuracy of 98.3 per cent. These optimised computational tools support proactive crop disease management, helping to mitigate harvest losses and advance sustainable farming practices.
Early detection of potato blights helps prevent widespread harvest failures and protects food quality. By linking standard weather measurements directly to disease emergence, growers can receive early warnings and intervene before infections spread. This data-driven approach promotes proactive disease management, reduces unnecessary chemical treatments, and supports more resilient and sustainable agricultural practices.
This work demonstrates an early-stage computational approach for agricultural disease prediction. The models could inform decision-support software and advisory platforms used by potato farmers, agricultural extension workers, and agronomic service providers. Moving this from retrospective dataset analysis to a near-market commercial tool will require testing the algorithms with real-time local weather forecasts and validating automated alerts in live field environments.
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Abstract The diseases that particularly affect potato leaves are early blight and the late blight, and they are dangerous as they reduce yield and quality of the potatoes. In this paper, different machine learning (ML) models for predicting these diseases are analysed based on a detailed database of more than 4000 records of weather conditions. Some of the critical factors that have been investigated to determine correlations with disease prevalence include temperature, humidity, wind speed, and atmospheric pressure. These types of data relationships were comprehensively identified through sophisticated means of analysis such as K -means clustering, PCA, and copula analysis. To achieve this, several machine learning models were used in the study: logistic regression, gradient boosting, multilayer perceptron (MLP), and support vector machine (SVM), as well as K -nearest neighbor (KNN) models both with and without feature selection. Feature selection methods such as the binary Greylag Goose Optimization (bGGO) were applied to improve the predictive performance of the models by identifying feature sets pertinent to the models. Results demonstrated that the MLP model, with feature selection, achieved an accuracy of 98.3%, underscoring the critical role of feature selection in improving model performance. These findings highlight the importance of optimized ML models in proactive agricultural disease management, aiming to minimize crop loss and promote sustainable farming practices.
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DOI: 10.1007/s11540-024-09763-8
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