Crop leaf disease detection for beans using ensembled-convolutional neural networks

Crops' health is affected by a varied range of diseases. Convenient and precise diagnosis plays a substantial role in preventing the loss of crop quality. In the past decade, deep learning (DL), particularly Convolutional Neural Networks (CNNs), has presented extraordinary performance for diverse applications involving crop disease (CD) detection. In this study, a comparison is drawn for the three pre-trained state-of-art architectures, namely, EfficientNet B0, ResNet50, and VGG19. An ensembled CNN has also been generated from the mentioned CNNs, and its performance has been evaluated over the original coloured, grey-scale, and segmented dataset. K-means clustering has been applied with six clusters to generate the segmented dataset. The dataset is categorized into three classes (two diseased and one healthy class) of bean crop leaves images. The model performance has been assessed by employing statistical analysis relying on the accuracy, recall, F1-score, precision, and confusion matrix. The results have shown that the performance of ensembled CNNs' has been better than the individual pre-trained DL models. The ensembling of CNNs gave an F1-score of 0.95, 0.93, and 0.97 for coloured, grey-scale, and segmented datasets, respectively. The predicted classification accuracy is measured as: 0.946, 0.938, and 0.971 for coloured, grey-scale, and segmented datasets, respectively. It is observed that the ensembling of CNNs performed better than the individual pre-trained CNNs.