Browsing by Author "Kenduiywo, B. K."

Now showing 1 - 2 of 2

A Backward Regressed Capsule Neural Network for Plant Leaf Disease Classification
(Heliyon, 2021) Mugo, D. M.; Kenduiywo, B. K.; Too, E. C.
This study investigated the introduction of backward regression coupled with DenseNet features into a Capsule Neural Network (CapsNet) for plant leaf disease classification. Plant diseases are considered one of the main factors influencing food production, and therefore fast crop diseases detection and recognition is important in enhancing interventions. In the recent past, CapsNets have been used for plant leaf disease classification with some success. However, back propagation of signals to earlier layers is still a challenge due to low gradient flow, parameter and computational complexities exist due to lack of feature diversification which leads to poor patterns, and uses only higher level features while all features are necessary for classification. This work therefore adopted DenseNet intuition where a loop connectivity pattern was done in the convolution layer, a technique that made it easier for signals to be back propagated and create a strong gradient flow. The resultant model was able to attain computational and parameter efficiency because feature diversification led to richer patterns hence higher accuracy. The resultant model maintained low complexity as it used both complex and simple features. After feature collected in the convolution layer, backward regression was introduced to select only the features that had significant information to be used by the model, a technique that reduced computation time and reduced characters in the model without the loss of data. This work used the standard PlantVillage (PV) dataset comprising of ten tomato classes with a total of 9080 images and observed 99% accuracy on testing with backward regression and 87% on testing without backward regression.
A Backward Regressed Capsule Neural Network for Plant Leaf Disease Detection
(Science Publications, 2022) Jepkoech, J.; Kenduiywo, B. K.; Mugo, D. M.; Too, E. C.
This study investigated the introduction of backward regression coupled with DenseNet features in a Capsule Neural Network (CapsNet) for plant leaf disease classification. Plant diseases are considered one of the main factors influencing food production and therefore fast crop disease detection and recognition are important in enhancing food security interventions. CapsNets have successfully been adopted for plant leaf disease classification however, backpropagation of signals to preceding layers is still a challenge due to low gradient flow. In addition, parameter and computational complexities exist due to complex features. Therefore, this study implemented a loop connectivity pattern to improve gradient flow in the convolution layer and backward regression for feature selection. We observed a 99.7% F1 score with backward regression and 87% F1 score without backward regression accuracy on testing our framework based on the standard Plant Village (PV) dataset comprising ten tomato classes with 9080 images. Additionally, CapsNet with backward regression showed relatively higher and stable accuracy when sensitivity analysis was performed by varying testing and training dataset percentages. In comparison Support Vector Machines (SVM), Artificial Neural Networks (ANN), AlexNet, ResNet, VGGNet, Inception V3, and VGG 16 deep learning approaches scored 84.5, 88.6, 99.3, 97.87, 99.14, and 98.2%, respectively. These findings indicate that the introduction of backward regression of features in the CapsNet model may be a decent and, in most cases superior and less expensive alternative for phrase categorization models based on CNNs and RNNs. Therefore, the accuracy of plant disease detection may be enhanced even further with the aid of the fusion of several classifiers and the integration of a backward regressed capsule neural network.