# Automatic Identification of Peanut-Leaf Diseases Based on Stack Ensemble
## 5 Conclusions  
Peanut-leaf disease is an important reason for the decline of peanut yields and quality. If a disease of peanut leaves can be detected in time, preventive measures can be taken as soon as possible to reduce the loss of crops.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**AlexNet**} & \multicolumn{3}{c}{**VGG16**} & \multicolumn{3}{c}{**ResNet50**} & \multicolumn{3}{c}{**DenseNet121**} & \multicolumn{3}{c}{**InceptionV3**} \\ \cline{2-13}  & **Recall** & **Precision** & **Meta\({}^{\,1}\)** & **Recall** & **Precision** & **Meta** & **Recall** & **Precision** & **Meta** & **Recall** & **Precision** & **Meta** \\ \hline HD & 99.38 & 97.25 & **RF** & 99.53 & 98.76 & **LR** & 99.69 & 99.94 & **LR** & 99.53 & 99.84 & **LR** & 100.00 & 99.53 & **LR** \\ SD & 75.00 & 88.46 & **RF** & 85.87 & 92.94 & **LR** & 93.48 & 95.56 & **SVM** & 95.65 & 93.62 & **SVM** & 92.39 & 94.44 & **LR** \\ RD & 97.06 & 90.10 & **LR** & 96.32 & 95.62 & **RF** & 98.53 & 96.75 & **LR** & 98.53 & 96.75 & **RF** & 97.79 & 94.66 & **RF** \\ SD & & & & & & & & & & & & & & \\ + & 81.54 & 92.98 & **LR** & 92.31 & 90.91 & **RF** & 93.08 & 94.53 & **LR** & 90.77 & 95.93 & **LR** & 90.00 & 94.35 & **SVM** \\ RD & & & & & & & & & & & & & & \\ LSD & 71.01 & 75.38 & **LR** & 84.06 & 87.88 & **RF** & 91.30 & 90.00 & **RF** & 89.86 & 92.54 & **RF** & 85.51 & 88.06 & **RF** \\ \hline \hline \end{tabular} \({}^{1}\) Meta-model.  
\end{table}
Table 5: Recall and precision of the best ensemble for each class.  
To solve the problem of peanut-leaf diseases, this paper combined deep-learning models and traditional machine-learning methods to identify four peanut-leaf diseases. The networks with deeper layers, like ResNet50 and DenseNet121, had the best prediction performance for datasets. In the case of data augmentation, the highest accuracy reached 97.59%, and the \(F1\) score reached 90.50 using the traditional machine-learning method for the ensemble.  
In this paper, a leaf showing two diseases, a mixture of rust and scorch diseases, was identified for the first time. The best identification performance was achieved by ResNet50 combined with the LR model, which had a recall rate of 93.08, and the corresponding \(F1\) score of 93.80 was the highest among all models. The other models also achieved good identification performance, indicating that the machine-learning method is still a powerful tool for identifying peanut-leaf diseases, including in the case of a peanut leaf showing two diseases at the same time.  
At present, although the laboratory model of the highest recognition rate reached 97.59%, the field environment is complicated, and recognition of peanut-leaf diseases will still encounter many problems, such as a peanut leaf appearing at the same time as a number of different leaves, the degree of disease occurring, and the difficulty of training data collection. Further studies are needed to improve these problems.  
Conceptualization, H.Q.; methodology, H.Q.; software, Y.L.; validation, J.Z.; formal analysis, Y.L., Q.D.; investigation, Y.L.; resources, Y.L.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, H.Q., Q.D.; visualization, J.Z.; supervision, H.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key-Area Research and Development Program of Guangdong Province (2019B020214003), and characteristic innovation projects of the Guangdong Provincial Department of Education in 2019 (2019 KTSCX015).  
Not applicable.  
The authors declare no conflict of interest.