AIMC Topic: Plant Leaves

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Spatial attention-guided pre-trained networks for accurate identification of crop diseases.

Scientific reports
The maintenance of agricultural productivity is critically dependent on the efficient and accurate identification of plant diseases. As observed, the manual inspection to the illness is often inefficient and error-prone, particularly under conditions...

Hybrid machine learning and physics-based model for estimating lettuce (Lactuca sativa) growth and resource consumption in aeroponic systems.

Scientific reports
As the global population is expected to reach 10.3 billion by the mid-2080s, optimizing agricultural production and resource management is crucial. Climate change and environmental degradation further complicate these challenges, impacting crop produ...

Monitoring and predicting cotton leaf diseases using deep learning approaches and mathematical models.

Scientific reports
Cotton, the backbone of global textile production, demands sustainable agricultural practices to ensure fiber, food, and environmental security. Cotton crop play an essential role in farming economies; however, production is sometimes affected by var...

Multiclass semantic segmentation for prime disease detection with severity level identification in Citrus plant leaves.

Scientific reports
Agriculture provides the basics for producing food, driving economic growth, and maintaining environmental sustainability. On the other hand, plant diseases have the potential to reduce crop productivity and raise expenses, posing a risk to food secu...

Plant leaf disease detection using vision transformers for precision agriculture.

Scientific reports
Plant diseases cause major crop losses worldwide, making early detection essential for sustainable farming. Traditional methods need large training datasets, are expensive, and may overfit. In leaf image analysis, convolutional neural networks (CNNs)...

A machine-learning-powered spectral-dominant multimodal soft wearable system for long-term and early-stage diagnosis of plant stresses.

Science advances
Addressing the global malnutrition crisis requires precise and timely diagnostics of plant stresses to enhance the quality and yield of nutrient-rich crops, such as tomatoes. Soft wearable sensors offer a promising approach by continuously monitoring...

In situ foliar augmentation of multiple species for optical phenotyping and bioengineering using soft robotics.

Science robotics
Precision agriculture aims to increase crop yield while reducing the use of harmful chemicals, such as pesticides and excess fertilizer, using minimal, tailored interventions. However, these strategies are limited by factors such as sensor quality, w...

Mulberry leaf disease detection by CNN-ViT with XAI integration.

PloS one
Mulberry leaf disease detection is vital for maintaining the health and productivity of mulberry crops. In this paper, a novel approach was proposed by integrating explainable artificial intelligence (XAI) techniques with a convolutional neural netwo...

Assessing the performance of domain-specific models for plant leaf disease classification: a comprehensive benchmark of transfer-learning on open datasets.

Scientific reports
Agriculture and its yields are indispensable to human life all over the planet. It is an essential part of many countries' economies and without it the world's population can not be fed. As such, guaranteeing harvest with minimal loss is a primary ob...

Enhancing the dataset of CycleGAN-M and YOLOv8s-KEF for identifying apple leaf diseases.

PloS one
Accurate diagnosis of apple diseases is vital for tree health, yield improvement, and minimizing economic losses. This study introduces a deep learning-based model to tackle issues like limited datasets, small sample sizes, and low recognition accura...