STACKING ENSEMBLE OF LIGHTWEIGHT CONVOLUTIONAL NEURAL NETWORKS FOR PLANT LEAF DISEASE RECOGNITION

Abstract. The high-grade quality of agricultural goods can be affected by diseases.
Therefore, farmers need to quickly stop the spread of diseases. This study proposes a
stacking ensemble of lightweight learning convolutional neural network (CNN) framework
to enhance the recognition accuracy of plant leaf disease images. In the proposed framework, we first planned four lightweight CNN architectures (InceptionResNetV2, NASNetMobile, MobileNetV2, and EfficientNetB1) to train and create robust CNN models from
images of plant leaf diseases. The experimental results showed that the EfficientNetB1
outperformed other CNN models. We then created the stacking ensemble learning by
stacking the output probabilities of each CNN model and provided as output to train to
create the second model using the machine learning classifier. In this step, we experimented with five classifiers that were logistic regression, support vector machine, K-nearest
neighbors, random forest, and long short-term memory network. We found that the random forest method achieved a more accurate performance. As a result, we considered
that all machine learning techniques could be involved in stacking ensemble learning.
Keywords: Convolutional neural network (CNN), Lightweight CNN, Stacking ensemble learning method, Ensemble learning method, Meta-learner method, Plant leaf disease
recognition