Volume 20 No 12 (2022)
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Comparison and Analysis of CNN based Algorithms for Plant Disease Identification
Ritika Rattan and Jeba Shiney O
High crop yield is an important feature that impacts the field of agribusiness and farmers financially, socially, and in every perspective. At different stages of a crop's growth, it is important to keep a close eye on it so that early infections can be found. Manual examination of the crops cannot help because humans are always prone to errors, and the risk of false predictions is high, and it is very time consuming as well. Also, when the paradigm shift is towards smart agriculture these years, automation of every aspect of crop growing and monitoring is important. Therefore, in this work, we have analyzed the appropriateness of automated approach for the classification of diseases in plants. Two algorithms one based on a CNN, and other based on VGG-16 approach has been compared and analyzed in conditions of precision and loss. The performance has been verified with the plant village dataset and the precision given by first trained model of CNN (convolution neural network) was 96.77% and the VGG based trained model with batch normalization is 94%.
Convolution neural network (CNN), plant disease detection, village plant disease dataset
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