Disease Detection in Plum Using Convolutional Neural Network under True Field Conditions.

Abstract

The agriculture sector faces crop losses every year due to diseases around the globe, which adversely affect food productivity and quality. Detecting and identifying plant diseases at an early stage is still a challenge for farmers, particularly in developing countries. Widespread use of mobile computing devices and the advancements in artificial intelligence have created opportunities for developing technologies to assist farmers in plant disease detection and treatment. To this end, deep learning has been widely used for disease detection in plants with highly favorable outcomes. In this paper, we propose an efficient convolutional neural network-based disease detection framework in plum under true field conditions for resource-constrained devices. As opposed to the publicly available datasets, images used in this study were collected in the field by considering important parameters of image-capturing devices such as angle, scale, orientation, and environmental conditions. Furthermore, extensive data augmentation was used to expand the dataset and make it more challenging to enable robust training. Investigations of recent architectures revealed that transfer learning of scale-sensitive models like Inception yield results much better with such challenging datasets with extensive data augmentation. Through parameter quantization, we optimized the Inception-v3 model for deployment on resource-constrained devices. The optimized model successfully classified healthy and diseased fruits and leaves with more than 92% accuracy on mobile devices.

Plant phenotyping relevance

圃場画像からプラムの健全・罹病状態を推定するCNN手法を開発し、データ収集、頑健性向上、量子化、モバイル実装、精度評価まで扱っており、病害表現型の取得・判定が中心である。

Code and data availability

The paper's plum disease image dataset (5000 field images, augmented to 100,000) is not publicly available; authors only state a future intention to release it. No analysis code, trained model checkpoints, or supplementary data deposits are mentioned. Allowed URLs are generic references (WHO, Plantix, TensorFlow Lite,等

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