AIMC Topic: Plant Diseases

Clear Filters Showing 241 to 250 of 269 articles

Hyperspectral Imaging for Presymptomatic Detection of Tobacco Disease with Successive Projections Algorithm and Machine-learning Classifiers.

Scientific reports
We investigated the feasibility and potentiality of presymptomatic detection of tobacco disease using hyperspectral imaging, combined with the variable selection method and machine-learning classifiers. Images from healthy and TMV-infected leaves wit...

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification.

Computational intelligence and neuroscience
The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image...

NBSPred: a support vector machine-based high-throughput pipeline for plant resistance protein NBSLRR prediction.

Bioinformatics (Oxford, England)
UNLABELLED: The nucleotide binding site leucine-rich repeats (NBSLRRs) belong to one of the largest known families of disease resistance genes that encode resistance proteins (R-protein) against the pathogens of plants. Various defence mechanisms hav...

Identification of novel Xanthomonas euvesicatoria type III effector proteins by a machine-learning approach.

Molecular plant pathology
The Gram-negative bacterium Xanthomonas euvesicatoria (Xcv) is the causal agent of bacterial spot disease in pepper and tomato. Xcv pathogenicity depends on a type III secretion (T3S) system that delivers effector proteins into host cells to suppress...

Rapid and quantitative detection of Botryosphaeria dothidea by surface-enhanced Raman spectroscopy with size-controlled spherical metal nanoparticles combined with machine learning.

International journal of food microbiology
Botryosphaeria dothidea infection has become a major factor affecting the quality of postharvest fruits, so detection of B. dothidea infection is very important to control the spread of infection and ensure food safety. In this study, we built a moni...

Machine learning-driven GWAS uncovers novel candidate genes for resistance to frosty pod rot and witches' broom disease in cacao.

The plant genome
Cacao (Theobroma cacao), the source of chocolate, is threatened by devastating diseases like frosty pod rot (FPR) and witches' broom disease (WBD), impacting global production and farmer livelihoods. Here, we employ a machine learning-driven genome-w...

Detection of pine wood nematode infections in Chinese pine (Pinus tabuliformis) using hyperspectral drone images.

Pest management science
BACKGROUND: The pine wood nematode (PWN) has caused tremendous damage to pine forests in China. Accurately predicting the infestation stage of PWN is crucial for implementing appropriate management, such as chemically controlling early-infested trees...

On construction of data preprocessing for real-life SoyLeaf dataset & disease identification using Deep Learning Models.

Computational biology and chemistry
The vast volumes of data are needed to train Deep Learning Models from scratch to identify illnesses in soybean leaves. However, there is still a lack of sufficient high-quality samples. To overcome this problem, we have developed the real-life SoyLe...

An efficient plant disease detection using transfer learning approach.

Scientific reports
Plant diseases pose significant challenges to farmers and the agricultural sector at large. However, early detection of plant diseases is crucial to mitigating their effects and preventing widespread damage, as outbreaks can severely impact the produ...

Towards precision agriculture tea leaf disease detection using CNNs and image processing.

Scientific reports
In this study, we introduce a groundbreaking deep learning (DL) model designed for the precise task of classifying common diseases in tea leaves, leveraging advanced image analysis techniques. Our model is distinguished by its complex multi-layer arc...