Study on detecting diseased leaves using deep learning

Plants are an integral part of our ecosystem, and automation in detecting their diseases has intrigued researchers all around the world. In the proposed context, the research team illustrates a comparison analysis, to detect diseased leaf images of bell pepper and tomato plants. In this research, they have developed a custom-designed CNN architecture and a deep neural network, DenseNet121. The model was evaluated using standard parameters like precision, sensitivity, specificity, F-measure, FPR, and FNR, which guarantees the outperforming ability of a pre-trained classifier with respect to the custom CNN. The balanced accuracy (BAC) of CNN and DenseNet121 was 96.5% and 98.7%, respectively, thus outperforming all other works on this particular dataset. The train data size was 80%, and the test data size was 12% with validation as 8%.

Read the complete research at www.researchgate.net.

Ghosh, Sourodip. (2021). Detecting Diseased Leaves Using Deep Learning. 10.1007/978-981-33-4866-0_6. 
 


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