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.
The data contains 102 healthy, 208 stripe rust, and 97 septoria detected wheat leaf.
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 study used a dataset contains 1,266 healthy and (Stip Rust, Septoria) 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.
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.

埃塞俄比亚拥有丰富的种植多种作物的资源，因此小麦作为供应人口的主要作物之一，但其生长过程中却遭受了病毒、细菌和真菌的侵染。在本研究中，我们选取了两种主要病害——条锈病和叶枯病小麦叶片图像进行实验。该数据集包含102张健康叶片、208张条锈病叶片和97张叶枯病叶片。目前，上述小麦病害不仅成为农民的棘手问题，同时也给植物病理学家带来了挑战。此外，病理学家通常依靠肉眼观察进行病害检测，而有时在未使用实验室材料的情况下进行检测颇具挑战性。已有研究通过传统的机器学习算法进行病害识别和检测，但这些算法由于特征提取依赖于专家知识且所需数据处理量相对于机器学习而言较大，因而存在不足。因此，本研究采用了深度学习方法来检测小麦病害。该方法分为三个主要阶段。第一阶段是从小麦农场收集数据集，图像分为三类，即‘健康小麦叶片’、‘条锈病’和‘叶枯病’。然后采用80%-10%-10%的方法将数据集划分为训练集、验证集和测试集。第二阶段是通过实验寻找最佳超参数来设计神经网络。最后，选取最佳模型并使用未见过的图像数据进行测试。本研究使用的数据集包含1,266张健康和（条锈病、叶枯病）病害感染图像，其中80%用于训练，10%用于验证，剩余10%用于测试。在训练过程中，使用数据增强技术生成更多图像以适应所提出的模型。实验结果表明，所提出的模型在检测小麦叶片病害（条锈病和叶枯病）方面是有效的。实验中使用的预训练模型包括VGG19、InceptionV3、MobileNet和EfficientNet。在所提及的预训练模型中，MobileNet取得了最佳结果，模型能够以90%的测试准确率成功分类所提供的图像，这些图像是在真实小麦农场具有异质环境条件下拍摄的。数据收集地点位于埃塞俄比亚的Holeta小麦农场，在不受控制的自然环境中进行拍摄。此外，在植物病理学家的协助下，数据被分为三个类别：条锈病、叶枯病和健康。所使用的相机是Canon EOS 5D Mark III，这是一款高分辨率数码相机，能够清晰地显示叶片的细节。最终，该数据集有助于计算机视觉领域研究人员在植物病害检测方面的研究。