Results 51 to 60 of about 824 (172)
Current plant disease detection systems often overlook botanical expertise, limiting diagnostic accuracy. We propose SwiftM, a novel framework that integrates plant anatomical knowledge into deep learning through botanical knowledge distillation, structure‐aware graph representation and sparse attention over botanical graphs.
Sumaya Mustofa, Yousuf Rayhan Emon
wiley +1 more source
Suppression of Two Tungro Viruses in Rice by Separable Traits Originating from Cultivar Utri Merah
Rice tungro disease (RTD) is caused by Rice tungro spherical virus (RTSV) and Rice tungro bacilliform virus (RTBV) transmitted by green leafhoppers. Rice cv. Utri Merah is highly resistant to RTD. To define the RTD resistance of Utri Merah, near-isogenic
Jaymee R. Encabo +10 more
doaj +1 more source
Presence of Rice Tungro Bacilliform Virus (RTBV) in Xylem Cells of Tungro-Infected Rice
This article 'Presence of Rice Tungro Bacilliform Virus (RTBV) in Xylem Cells of Tungro-Infected Rice' appeared in the International Rice Research Newsletter series, created by the International Rice Research Institute (IRRI). The primary objective of this publication was to expedite communication among scientists concerned with the development of ...
Sta. Cruz, F. C., Koganezawa, H.
openaire +2 more sources
Plant diseases represent a major challenge to global agricultural productivity and require precise and rapid diagnostic tools for effective intervention. This study presents an adaptive attention‐enhanced multiscale vision transformer (MS‐ViT) framework for plant disease classification.
Xuan Yang +6 more
wiley +1 more source
Mathematical Models for the Prevention and Management of Cereal Crop Diseases: A Systematic Review
Mathematical modeling plays an important role in understanding and managing diseases that threaten cereal crop production worldwide. This systematic review, conducted in accordance with PRISMA guidelines, examines mathematical models developed for the prevention and management of diseases affecting maize, rice, wheat, barley, and sorghum.
Furaha Michael Chuma, Deli Zhang
wiley +1 more source
This study proposes a NN‐based framework for automated recognition and classification of rice diseases using leaf imagery. Feature extraction techniques such as texture analysis, GLCM, GLDM, FFT, and DWT extract critical image characteristics, while dimensionality reduction (PCA, KPCA, Sparse AE, Stacked AE) and feature selection (ANOVA F‐measure, Chi ...
Farida Siddiqi Prity +5 more
wiley +1 more source
Rice Tungro disease is one of biotic constraint that can reduce yield potential of rice. Tungro disease viruses are transmitted from one plant to another by leafhoppers that feed on tungro-infected plants.
Dini Yuliani
doaj
This article 'Varietal Resistance to Tungro' appeared in the International Rice Research Newsletter series, created by the International Rice Research Institute (IRRI). The primary objective of this publication was to expedite communication among scientists concerned with the development of improved technology for rice and for rice based cropping ...
openaire +1 more source
Hybrid AI Model With CNNs and Vision Transformers for Precision Pest Classification in Crops
This study proposes a hybrid deep learning model integrating CNNs, attention mechanisms, and Vision Transformers for accurate classification of five major rice crop pests. The model captures both fine‐grained local features and global spatial relationships, achieving a high accuracy of 95% on an augmented dataset.
Neha Sharma +4 more
wiley +1 more source
A New Symptom of Tungro in Rice
This article 'A New Symptom of Tungro in Rice' appeared in the International Rice Research Newsletter series, created by the International Rice Research Institute (IRRI). The primary objective of this publication was to expedite communication among scientists concerned with the development of improved technology for rice and for rice based cropping ...
Kobayashi, N. +3 more
openaire +2 more sources

