Classification of pumpkin disease by using a hybrid approach

Abstract

Agricultural products are crucial for the long-term sustainability of developing economies. Agriculture is a vital sector for most developing countries, including Ethiopia. The pumpkin crop is one of the most significant agricultural products globally in terms of human food security. However, it is susceptible to several diseases, including pumpkin common rust diseased leaf, pumpkin Downy mildew diseased leaf, and pumpkin fruit rot, among others. Traditionally, farmers identify diseases through visual observation, which is both inaccurate and time-consuming. Previous research has indicated that binary classification has to be improved because some classes were more difficult to detect. In this paper, we proposed a hybrid method that incorporates the extract features of the ResNet and LeNet networks to construct a pumpkin disease classification model. Dataset gathering, image preprocessing, segmentation, augmentation, feature extraction, and classification are all processes in the proposed hybrid CNN ResLeNet model. Finally, the suggested hybrid method CNN model was assessed, yielding 99.78 % training accuracy, 98.18 % validation accuracy, and 97.21 % testing accuracy1. The study's findings suggest that the proposed hybrid model is suitable for recognizing pumpkin leaf and fruit diseases from digital images of pumpkin.