Hybrid Cassava Identification from Morphometric Analysis to Deep Convolutional Neural Networks and Confirmation Strategies

The correct identification of cassava varieties is critical for crop management, such as for developing high-value products or against agricultural pests. In this study, plant characteristic regions used for classification were verified by principal component analysis (PCA) techniques. A deep learning method was applied using well-known pretrained models to identify hybrid cassava through image classification. The models employed—ResNet-18, VGG-16, AlexNet, and GoogLeNet—yielded impressive accuracies in three-fold cross-validation experiments, achieving 100, 99.06, 99.06, and 98.59 % averaged accuracy, respectively. The fine-tuned ResNet-18 model had the highest accuracy and was selected for identifying hybrid cassava. A confusion matrix revealed 3 misidentifications. Cultivar variety (cv) R72 was mistakenly classified as R5 in both the 1st and 2nd folds and as R1 in the 2nd fold. Additionally, we utilized Local Interpretable Model-agnostic Explanations (LIME) to ensure that our models offered insightful explanations for their decision-making processes. The outcomes from Principal Component Analysis (PCA) and Local Interpretable Model-agnostic Explanations (LIME) exhibited close resemblance, particularly within the region encompassing the stem, branch, petiole, and stipule of cassava. These findings were leveraged for the identification of the 3 distinct cultivated cassava varieties. The results demonstrated the efficacy of deep learning as a potent technique for discerning hybrid cassava varieties, presenting promising prospects for its practical deployment in on-site testing and widespread adoption due to its time-saving capabilities. HIGHLIGHTS A deep learning method was applied to identify cassava with similar genetic varieties through image classification. High performance was achieved using a fine-tuned ResNet-18 model achieving 100 % accuracy for identifying hybrid cassava. The LIME technique was employed to generate interpretable explanations for individual predictions. GRAPHICAL ABSTRACT