AIMC Topic: Plant Leaves

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Hyperspectral imaging for trace cadmium prediction in lettuce leaves.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Cadmium (Cd) pollution presents a significant threat to the agricultural product control, and the development of detection technology for Cd content in lettuce has important application value. This study developed a nondestructive approach based on h...

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...

Monochromatic LeafAdaptNet (MLAN): an adaptive approach to spinach leaf disease detection using monochromatic imaging.

World journal of microbiology & biotechnology
A country's economic growth heavily relies on agricultural productivity, specifically nutrition derived from vegetables and leafy greens. Spinach, abundant in iron, vitamins, and other essential nutrients, plays a vital role in maintaining the health...

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)...