Detection and monitoring of these rice plant diseases is a critical issue. Rice plants are affected in various kind of disease like hispa, brown spot, and leaf blast and show the syndrome in the leaf of these diseases. If these diseases are detected early and take appropriate action, it ...
Segmentation is carried out using K-mean clustering algorithm to acquire the infected portion of leaf. The texture feature vectors which were extracted from the segmented images were given as an input to the classifier. The Support Vector Machine is able to classify the disease more accurately (...
Rice disease recognition is crucial in automated rice disease diagnosis systems. At present, deep convolutional neural network (CNN) is generally considered the state-of-the-art solution in image recognition. In this paper, we propose a novel rice blast recognition method based on CNN. A dataset ...
D. Raj, "Deep learning system for paddy plant disease detection and classification," Environ Monit Assess, vol. 195, no. 1, p. 120, Jan. 2023. [8] R. P. Narmadha, N. Sengottaiyan, and R. J. Kavitha, "Deep Transfer Learning Based R...
Sheath blight (ShB) disease caused by Rhizoctonia solani Kühn, is one of the most economically damaging rice (Oryza sativa L.) diseases worldwide. There are no known major resistance genes, leaving only partial resistance from small-effect QTL to deploy
Extensive researches on identification of genetic variants for economically important traits have been performed using single-locus (SL) methodologies including MLM [47] and CMLM [48] that have limited quantitative trait nucleotides (QTNs) detection ability due to their polygenic nature and conservative...
In addition, due to its read length, short-read sequencing data may have lower sensitivity for SV detection compared with long-read sequencing data. PAVs provide insights into rice population structure Most population structure studies are currently performed using SNPs [40]; however, structural ...
images of Rice Hispa, we observed a significant increase in the classification accuracy over uncropped test samples. Hence, we propose that real-world implementations of rice disease detection using convolutional neural networks must crop the test images to remove background noise in order to improve...
2014). Recently there are successful examples of abiotic trait improvement in rice using genome editing which include enhanced cold tolerance by using TIFY1b transcription factor gene editing using Nipponbare rice variety (Khang 2018), improved blast disease resistance in rice variety Kuiku131 by ...
A CBAM-CARAFE-DeepLabv3+ method for rice disease segmentation was proposed.To improve the accuracy of disease recognition, the algorithm adopts CBAM-RepViT as the backbone network.An efficient and lightweight CARAFE operator is introduced into the decoding module for upsampling.A hybrid loss function...