Apple diseases: detection and classification using transfer learning

Assif Assad, M. Bhat, Z. A. Bhat, Ab Naffi Ahanger, Majid Kundroo, R. Dar, Abdul Basit Ahanger, B. N. Dar
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Abstract

Human diagnosis of horticultural diseases comes with added monetary costs in the shape of time, cost, and acces-sibility, with still considerable possibilities of misdiagnosis. Most common plant diseases present visually recognizable symptoms like change in color, shape, or texture. Deep learning is known to work with such accuracy and precision in recognizing patterns in such visual symptoms that rivals human diagnosis. We specifically designed a deep learning–based multi-class classification model AppleNet to include extra apple plant diseases, which has not been the case with other previously designed models. Our model takes advantage of transfer learning techniques by implementing ResNET 50 Convolutional Neural Network pretrained on image-net dataset. The knowledge of features learned by ResNET 50 is being used to extract features from our dataset. This technique takes advantage of knowledge learned on a larger and more diverse dataset and also saves precious computational resources and time in training on a relatively lesser data. The hyper-parameters were uniquely fine-tuned to maximize the model efficiency. We created our own dataset from the images taken directly from the trees, which, unlike the publicly available datasets created in a controlled setting with smooth (white) background, has been created in a real world environment and includes background noise as well. This helped us train our model in a more realistic way. The results of experimentation on a collected dataset of 2897 images with data augmentation demonstrated that AppleNet can be efficiently used for apple disease detection with a classification accuracy of 96.00%. To examine the effectiveness of our proposed approach, we compared our model with other pretrained models and a baseline model created from scratch. Results of the experiment demonstrate that transfer learning improves the performance of deep learning models and using pretrained models based on residual neural network architectures gives remarkable results as compared to other pretrained models. The mean difference in classification accuracies between our proposed model AppleNet and other experimental models was 21.54%.
苹果病害:利用迁移学习的检测与分类
人类对园艺疾病的诊断伴随着时间、成本和可及性方面的额外金钱成本,并且仍然有相当大的误诊可能性。大多数常见的植物病害表现为视觉上可识别的症状,如颜色、形状或质地的变化。众所周知,深度学习在识别视觉症状模式方面的准确性和精确性可以与人类的诊断相媲美。我们专门设计了一个基于深度学习的多类分类模型AppleNet,它包含了额外的苹果植物病害,这是以前设计的其他模型所没有的。我们的模型通过在image-net数据集上实现ResNET 50卷积神经网络来利用迁移学习技术。ResNET 50学习到的特征知识被用来从我们的数据集中提取特征。这种技术利用了在更大、更多样化的数据集上学习到的知识,并且在相对较少的数据集上节省了宝贵的计算资源和训练时间。超参数进行了独特的微调,以最大限度地提高模型效率。我们从直接从树上获取的图像中创建了自己的数据集,这与在光滑(白色)背景的受控设置中创建的公开可用数据集不同,它是在现实世界环境中创建的,并且包含背景噪声。这有助于我们以更现实的方式训练我们的模型。在收集的2897张图像数据集上进行数据增强的实验结果表明,AppleNet可以有效地用于苹果病害检测,分类准确率达到96.00%。为了检验我们提出的方法的有效性,我们将我们的模型与其他预训练模型和从头创建的基线模型进行了比较。实验结果表明,迁移学习提高了深度学习模型的性能,与其他预训练模型相比,使用基于残差神经网络架构的预训练模型效果显著。我们提出的模型AppleNet与其他实验模型的分类准确率平均相差21.54%。
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