AIMC Topic: Plant Leaves

Clear Filters Showing 31 to 40 of 329 articles

Foliar disease resistance phenomics of fungal pathogens: image-based approaches for mapping quantitative resistance in cereal germplasm.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Host plant resistance is the most effective and environmentally sustainable means of reducing yield losses caused by fungal foliar pathogens of cereal species. Cereal genebank collections hold diverse pools of potentially underutilized disease resist...

The analysis of landscape design and plant selection under deep learning.

Scientific reports
This paper explores the application of deep learning (DL) techniques in landscape design and plant selection, aiming to enhance design efficiency and quality through automated plant leaf image recognition (PLIR). A novel framework based on Convolutio...

Multi-kernel inception-enhanced vision transformer for plant leaf disease recognition.

Scientific reports
The timely and precise identification of diseases in plants is essential for efficient disease control and safeguarding of crops. Manual identification of diseases requires expert knowledge in the field, and finding people with domain knowledge is ch...

Enhanced residual-attention deep neural network for disease classification in maize leaf images.

Scientific reports
Disease classification in maize plant is necessary for immediate treatment to enhance agricultural production and assure global food sustainability. Recent advancements in deep learning, specifically convolutional neural networks, have shown outstand...

Lightweight grape leaf disease recognition method based on transformer framework.

Scientific reports
Grape disease image recognition is an important part of agricultural disease detection. Accurately identifying diseases allows for timely prevention and control at an early stage, which plays a crucial role in reducing yield losses. This study addres...

Ensemble-based sesame disease detection and classification using deep convolutional neural networks (CNN).

Scientific reports
This study presents an ensemble-based approach for detecting and classifying sesame diseases using deep convolutional neural networks (CNNs). Sesame is a crucial oilseed crop that faces significant challenges from various diseases, including phyllody...

YOLO-LeafNet: a robust deep learning framework for multispecies plant disease detection with data augmentation.

Scientific reports
Plant diseases significantly harm crops, resulting in significant economic losses across the globe. In order to reduce the harm that these diseases produce, plant diseases must be diagnosed accurately and timely manner. In this work, a YOLO-LeafNet a...

Deep learning-based automatic diagnosis of rice leaf diseases using ensemble CNN models.

Scientific reports
Rice diseases pose a critical threat to global crop yields, underscoring the need for rapid and accurate diagnostic tools to ensure effective crop management and productivity. Traditional diagnostic approaches often lack both precision and scalabilit...

A comprehensive analysis of YOLO architectures for tomato leaf disease identification.

Scientific reports
Tomato leaf disease detection is critical in precision agriculture for safeguarding crop health and optimizing yields. This study compares the latest YOLO architectures, including YOLOv8, YOLOv9, YOLOv10, YOLOv11, and YOLOv12, using the Tomato-Villag...

An automated hybrid deep learning framework for paddy leaf disease identification and classification.

Scientific reports
In India, agriculture remains the primary source of livelihood for many people. Pathogen attacks in crops and plants significantly diminish both the yield and quality of production, leading to financial losses. As a result, identifying diseases in cr...