Plant disease detection with modified deep joint segmentation and combined GoogleNet-IRNN

Productivity in agriculture plays a major role in economic expansion. Because plant disease is a widespread occurrence, plant disease detection is an important problem in the world of agriculture. Plants do suffer a significant consequence if the required care is not taken at the beginning, which affects the amount, quality or productivity of the relevant products. Because it can detect disease symptoms at the earliest stage and reduces the labour required for large crop farm tracking, the automated plant disease detection system is more advantageous. In order to detect plant diseases, this paper proposes a novel, four-step methodology that consists of improved deep joint image segmentation, feature extraction (which includes LGXP, MBP, colour feature and hierarchy of skeleton feature extraction) and detection via hybrid DL classifier, specifically improved RNN with the transfer learning process and GoogleNet. By averaging the classifiers' results scores, the final detection result is calculated. In terms of several performance metrics, the suggested work's effectiveness is verified in comparison to the traditional models. In contrast to the SVM (79.5597), KNN (59.2767), LSTM (78.1446), GoogleNet (79.4025), CNN (77.6729), and CAE + CNN (80.1886), the F-measure of the IRNN-TL is 91.1949.