Abstract
Early prediction of pathogen infestation is a key factor to reduce the disease spread in plants. Macrophomina phaseolina (Tassi) Goid, as one of the main causes of charcoal rot disease, suppresses the plant productivity significantly. Charcoal rot disease is one of the most severe threats to soybean productivity. Prediction of this disease in soybeans is very tedious and non-practical using traditional approaches. Machine learning (ML) techniques have recently gained substantial traction across numerous domains. ML methods can be applied to detect plant diseases, prior to the full appearance of symptoms. In this paper, several ML techniques were developed and examined for prediction of charcoal rot disease in soybean for a cohort of 2,000 healthy and infected plants. A hybrid set of physiological and morphological features were suggested as inputs to the ML models. All developed ML models were performed better than 90% in terms of accuracy. Gradient Tree Boosting (GBT) was the best performing classifier which obtained 96.25% and 97.33% in terms of sensitivity and specificity. Our findings supported the applicability of ML especially GBT for charcoal rot disease prediction in a real environment. Moreover, our analysis demonstrated the importance of including physiological featured in the learning. The collected dataset and source code can be found in https://github.com/Elham-khalili/Soybean-Charcoal-Rot-Disease-Prediction-Dataset-code.
Highlights
The production of global crops has to be doubled by 2050 to meet the increasing needs of the world’s population (Khalili et al, 2019)
Multilayer Perceptron (MLP) performed the worst in terms of all the evaluation criteria with the lowest accuracy (94.88%), sensitivity (94.83%), specificity (94.92%), precision (94.72%), Negative Predictive Value (NPV) (95.06%), F1 score (94.77%), and Matthews Correlation Coefficient (MCC) (89.76%).The final analysis shows that Gradient Tree Boosting (GBT) classifier performed the best with the highest classification accuracy (96.79%), specificity (97.33%), precision (97.16%), NPV(96.49%), F1 score (96.68%), and MCC (93.62%)
It could be summarized that the GBT and support vector machines (SVM) models outperformed the other six models for the prediction of charcoal rot disease
Summary
The production of global crops has to be doubled by 2050 to meet the increasing needs of the world’s population (Khalili et al, 2019). ML for Rot Disease Prediction early detection of diseases is of a key importance to prevent disease spread and reduce damage to crop production (Martinelli et al, 2015). Macrophomina phaseolina (Tassi) Goid causes rot diseases in about 700 plant species. It is an extremely robust soil-borne fungus that damages several crops i.e., cotton, grains, oilseeds, legumes, jute along with fruits and vegetable plants (Ambrosio et al, 2015; Sun et al, 2016). Other symptoms may include the development of “blackleg” in infected plants which results in weaker plants and lower productivity (Santos et al, 2016). The infected plants die due to various reasons such as vascular blockages that weaken the nutrient transport (Santos et al, 2016) or exposure to phytotoxic metabolites released by M. phaseolina
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