Wheat Leaf Dataset
Ethiopia has a huge resource for planting several crops, thus wheat is one of the major crops which feed the population, but the crop has been infected by viruses, Bacteria, and Fungi. In this study, the two major diseases namely Stripe Rust and Septoria wheat leaf image were used for experimentation. Currently, the wheat disease mentioned above is a major headache not only for farmers but also for plant pathologists. Furthermore, the pathologist uses the naked eye observation for the detection of wheat disease, sometimes it is challenging to detect without using the laboratory material. Several types of research conducted disease identification and detection using the traditional machine learning algorithms. Thus algorithms have drawbacks since the feature extraction is expert-based and the amount of data processing required is high relative to the machine learning. Because of this, the deep learning methodology was used to detect wheat disease. The approach has three main phases. The first phase is to collect the dataset from the wheat farm, and the image has three categories i.e. ‘Healthy Wheat Leaf’, ‘Strip Rust’, and ‘Septoria Disease’. Then the dataset is partitioned using the 80%-10%-10% approach which is used for training, validation, and testing respectively. The second phase is to design a neural network by experimenting with the best hyperparameter. Finally, the best model was selected and tested with unseen image data. The dataset contains 208 stripe rust affected leaf, 102 healthy leaf and 97 septoria affected leaf disease-infected image pictures. From this, 80% of the images were used for training, l0% were used for validation, and the remaining 10% were used for testing. During training, the data augmentation technique is used to generate more images to fit the proposed model. The experimental result demonstrates that the proposed model is effective for the detection of wheat leaf disease (Strip Rust and Septoria). The pretrained model used for experimentation are VGG19, InceptionV3, MobileNet, and EfficientNet. Among mentioned pretrained models MobileNet has achieved the best result and the model can successfully classify the given image with a testing accuracy of 90% with images captured in the real wheat farm with a heterogeneous environment.
Steps to reproduce
The location of the data collection is at Holeta wheat farm, Ethiopia, and it was captured in a real wheat farm in an uncontrolled environment. Besides, it was sorted into three classes with the assistance of plant pathologists: the classes are Stripe Rust, Septoria, and Healthy. The camera used is Canon EOS 5D Mark III, it is a high-resolution digital camera capable of showing the detail of the leaf. Finally, the dataset could help researchers in the field of computer vision for plant disease detection research.