Plant diseases detection and classifications using SympDense- A fine-tuning deep learning model

Abstract

A nation’s progress is directly linked to the effective functioning of its agricultural sector. The detection and classification of plant disease is an essential component of the agricultural industry. Plant diseases may result in substantial financial losses due to decreased crop production. As per the Food and Agriculture Organization of the United Nations, it is estimated that plant diseases result in a reduction of approximately 10-16% in global crop yields annually. Farmers are traditionally relying on visual inspection, using naked eye observation, as the primary method for detecting plant diseases. This involves a meticulous examination of crops to identify any visible signs of diseases. However, manual disease detection can lead to delayed identification, resulting in significant crop losses. Various methods, coupled with machine learning classifiers, were demonstrated effectiveness in scenarios involving manual feature extraction and limited datasets. However, to handle larger datasets, deep learning models such as Inception V4, ResNet-152, EfficientNet-B5, and DenseNet-201 were studied and implemented. Among these models, DenseNet-201 exhibited superior performance and accuracy compared to the previous methodology. Additionally, A Fine-tuning Deep Learning Model called SympDense was developed, which surpassed other deep learning models in terms of accuracy.