AIMC Topic: Plant Leaves

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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...

A fine tuned EfficientNet-B0 convolutional neural network for accurate and efficient classification of apple leaf diseases.

Scientific reports
Precise classification and detection of apple diseases are essential for efficient crop management and maximizing yield. This paper presents a fine-tuned EfficientNet-B0 convolutional neural network (CNN) for the automated classification of apple lea...

ST-CFI: Swin Transformer with convolutional feature interactions for identifying plant diseases.

Scientific reports
The increasing global population, coupled with the diminishing availability of arable land, has rendered the challenge of ensuring food security more pronounced. The prompt and precise identification of plant diseases is essential for reducing crop l...

Combined impact of semantic segmentation and quantitative structure modelling of Southern pine trees using terrestrial laser scanning.

Scientific reports
Southern pine forests play a key role in the ecological function and economic vitality of the southeastern United States. High-resolution terrestrial laser scanning (TLS) has become an indispensable tool for advancing tree structural research and mon...