article · Smart Agricultural Technology
Pumpkins are vital for global food security and play a key role in developing economies such as Ethiopia. However, pumpkin crops face threats from diseases such as common rust, downy mildew, and fruit rot. Farmers conventionally rely on manual visual checks to spot these infections, but this approach is often inaccurate and slow. To improve diagnosis beyond conventional methods and binary classification, a hybrid convolutional neural network model named ResLeNet was developed. The system combines feature extraction capabilities from ResNet and LeNet architectures. The workflow encompasses gathering data, image preprocessing, segmentation, data augmentation, feature extraction, and disease classification. When evaluated, the hybrid network delivered strong diagnostic performance, reaching 99.78 percent training accuracy, 98.18 percent validation accuracy, and 97.21 percent testing accuracy on digital images of affected leaves and fruit.
Traditional manual observation of crop diseases is labour-intensive and frequently prone to error, potentially leading to widespread crop loss. By applying automated image recognition to detect specific pumpkin leaf and fruit infections with high accuracy, digital diagnostic tools can help farmers catch plant diseases earlier and protect food supplies more reliably.
This work could enable automated digital diagnostic tools, such as mobile or smart farming applications, for farmers and agricultural extension workers identifying pumpkin diseases. The research represents an applied and tested model based on digital images, though the abstract does not indicate whether it has been embedded into field-ready software or tested in real-world farm conditions.
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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.
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DOI: 10.1016/j.atech.2024.100398
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