AIMC Topic: Plant Diseases

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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...

DBA-ViNet: an effective deep learning framework for fruit disease detection and classification using explainable AI.

BMC plant biology
OBJECTIVE: The primary aim of this research is to develop an effective and robust model for identifying and classifying diseases in general fruits, particularly apples, guavas, mangoes, pomegranates, and oranges, utilizing computer vision techniques.

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 novel framework GRCornShot for corn disease detection using few shot learning with prototypical network.

Scientific reports
Precision and timeliness in the detection of plant diseases are important to limit crop losses and maintain global food security. Much work has been performed to detect plant diseases using deep learning methods. However, deep learning techniques dem...

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...

Integrating AI and CRISPR Cas13a for rapid detection of tomato brown rugose fruit virus.

Scientific reports
The Tomato Brown Rugose Fruit Virus (ToBRFV) has recently emerged as a serious threat to global tomato production, underscoring the need for rapid and sensitive diagnostic tools. Here, we present an AI-driven CRISPR-Cas13a pipeline for designing crRN...

AI-driven smart agriculture using hybrid transformer-CNN for real time disease detection in sustainable farming.

Scientific reports
Plant diseases pose a significant threat to global food security, with severe implications for agricultural productivity. Early and accurate detection of these diseases is crucial, yet it remains a challenging task, significantly impacting crop yield...

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...

Predicting yellow mosaic disease severity in yardlong bean using visible imaging coupled with machine learning model.

Scientific reports
Accurate estimation of plant disease severity is pivotal for effective management and decision-making. Field experiments were conducted to understand the correlation and predict the yellow mosaic disease severity in yard-long beans using visible imag...