Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique.

Abstract

Rice is one of the most widely consumed foods all over the world. Various diseases and deficiency disorders impact the rice crop's growth, thereby hampering the rice yield. Therefore, proper crop monitoring is very important for the early diagnosis of diseases or deficiency disorders. Diagnosis of diseases and disorders requires specialized manpower, which is not scalable and accessible to all farmers. To address this issue, machine learning and deep learning (DL)-driven automated systems are designed, which may help the farmers in diagnosing disease/deficiency disorders in crops so that proper care can be taken on time. Various studies have used transfer learning (TL) models in the recent past. In recent studies, further improvement in rice disease and deficiency disorder diagnosis system performance is achieved by performing the ensemble of various TL models. However, in all these DL-based studies, the segmentation of the region of interest is not done beforehand and the infected-region extraction is left for the DL model to handle automatically. Therefore, this article proposes a novel framework for the diagnosis of rice-infected leaves based on DL-based segmentation with bitwise logical AND operation and DL-based classification. The rice diseases covered in this study are bacterial leaf blight, brown spot, and leaf smut. The rice nutrient deficiencies like nitrogen (N), phosphorous (P), and potassium (K) were also included. The results of the experiment conducted on these datasets showed that the performance of DeepBatch was significantly improved as compared to the conventional technique.

Plant phenotyping relevance

イネ葉の病害・栄養欠乏という植物状態を対象に、感染領域のセグメンテーションと分類を組み合わせた画像解析手法を開発・評価しており、表現型取得が中心である。

Code and data availability

The paper's rice leaf disease and nutrient deficiency image datasets are publicly available on Kaggle, explicitly linked in the data availability statement. No author analysis code or trained models are publicly deposited (available only on request).