AIMC Topic: Plant Leaves

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Optimized deep learning network for plant leaf disease segmentation and multi-classification using leaf images.

Network (Bristol, England)
Automatic detection of plant diseases is very imperative for monitoring the plants because they are one of the major concerns in the agricultural sector. Continuous monitoring can combat diseases of plants, which contribute to production loss. In the...

Application of a U-Net Neural Network to the Maize Pathosystem.

Phytopathology
Computer vision approaches to analyze plant disease data can be both faster and more reliable than traditional, manual methods. However, the requirement of manually annotating training data for the majority of machine learning applications can presen...

Physics-assisted machine learning for THz time-domain spectroscopy: sensing leaf wetness.

Scientific reports
Signal processing techniques are of vital importance to bring THz spectroscopy to a maturity level to reach practical applications. In this work, we illustrate the use of machine learning techniques for THz time-domain spectroscopy assisted by domain...

Models for predicting coffee yield from chemical characteristics of soil and leaves using machine learning.

Journal of the science of food and agriculture
BACKGROUND: Coffee farming constitutes a substantial economic resource, representing a source of income for several countries due to the high consumption of coffee worldwide. Precise management of coffee crops involves collecting crop attributes (cha...

Optimal Enzyme-Assisted Extraction of Phenolics from Leaves of Pongamia pinnata via Response Surface Methodology and Artificial Neural Networking.

Applied biochemistry and biotechnology
This research work seeks to evaluate the impact of selected enzyme complexes on the optimised release of phenolics from leaves of Pongamia pinnata. After preliminary solvent extraction, the P. pinnata leaf extract was subjected to enzymatic treatment...

A quality grade classification method for fresh tea leaves based on an improved YOLOv8x-SPPCSPC-CBAM model.

Scientific reports
In light of the prevalent issues concerning the mechanical grading of fresh tea leaves, characterized by high damage rates and poor accuracy, as well as the limited grading precision through the integration of machine vision and machine learning (ML)...

A CNN-based model to count the leaves of rosette plants (LC-Net).

Scientific reports
Plant image analysis is a significant tool for plant phenotyping. Image analysis has been used to assess plant trails, forecast plant growth, and offer geographical information about images. The area segmentation and counting of the leaf is a major c...

Deep Learning Model for Classifying and Evaluating Soybean Leaf Disease Damage.

International journal of molecular sciences
Soybean ( (L.) Merr.) is a major source of oil and protein for human food and animal feed; however, soybean crops face diverse factors causing damage, including pathogen infections, environmental shifts, poor fertilization, and incorrect pesticide us...

Application technology for bioherbicides: challenges and opportunities with dry inoculum and liquid spray formulations.

Pest management science
Bioherbicides offer many potential benefits as part of an integrated weed management system or a totally biological or organic cropping system. A key factor for success is the selection of appropriate formulation and delivery systems for each target ...

Plant leaf infected spot segmentation using robust encoder-decoder cascaded deep learning model.

Network (Bristol, England)
Leaf infection detection and diagnosis at an earlier stage can improve agricultural output and reduce monetary costs. An inaccurate segmentation may degrade the accuracy of disease classification due to some different and complex leaf diseases. Also,...