REVIEW ON DETECTION OF RICE PLANT LEAVES DISEASES USING DATA AUGMENTATION AND TRANSFER LEARNING TECHNIQUES

Abstract

The most important cereal crop in the world is rice (Oryza sativa). Over half of the world's population uses it as a staple food and energy source. Abiotic and biotic factors such as precipitation, soil fertility, temperature, pests, bacteria, and viruses, among others, impact the yield production and quality of rice grain. Farmers spend a lot of time and money managing diseases, and they do so using a bankrupt "eye" method that leads to unsanitary farming practices. The development of agricultural technology is greatly conducive to the automatic detection of pathogenic organisms in the leaves of rice plants. Several deep learning algorithms are discussed, and processors for computer vision problems such as image classification, object segmentation, and image analysis are discussed. The paper showed many methods for detecting, characterizing, estimating, and using diseases in a range of crops. The methods of increasing the number of images in the data set were shown. Two methods were presented, the first is traditional reinforcement methods, and the second is generative adversarial networks. And many of the advantages have been demonstrated in the research paper for the work that has been done in the field of deep learning.

Plant phenotyping relevance

イネ葉の病害を画像分類・セグメンテーション等で検出・評価する手法をレビューしており、植物病態の画像ベース表現型解析が中心である。

Code and data availability

This is a review article on rice leaf disease detection using data augmentation and transfer learning. It describes third-party datasets (PlantVillage, Kaggle rice leaf datasets) and cites prior works, but provides no authors' own phenotype dataset, images, code, models, or supplements with explicit public availability

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