Effective multi-crop disease detection using pruned complete concatenated deep learning model

A significant threat to agriculture yield is crop disease. It leads to enormous losses for farmers and also has an impact economically. Leaves affected by certain diseases will exhibit unique characteristics, which will be utilized by deep learning frameworks to identify the diseases. In this paper, the Complete Concatenated Deep Learning (CCDL) architecture, a multi-crop disease detection model is proposed that is capable to classify the crop diseases irrespective of crops. In this architecture, Complete Concatenated Block (CCB) is introduced as a core functional unit. In this unit, the point convolution layer is positioned before every convolution layer to confine the number of parameters generated in the model. A complete concatenation path is invoked upon the convolution layers, contained within the CCB. It enhances the utilization of feature maps and helps to achieve better classification accuracy. The proposed architecture is trained using the reorganized Plant Village dataset. Later the trained model has been pruned for model size reduction, called Pruned Complete Concatenated Deep Learning model (PCCDL). This proposed architecture is delivered as three variants, in which the model PCCDL with Partial Standard Convolution Technique (PCCDL-PSCT) outperformed and achieved a higher classification accuracy of 98.14 % with a lesser model size of similar to 10 MB.

Automated summary

This paper proposes a multi-crop disease detection model using a complete concatenated deep learning architecture. The architecture introduces complete concatenated blocks as core functional units and reduces model size through pruning. The best performing model achieves a classification accuracy of 98.14% and has a smaller model size.