# Leaf Properties and Growth Measurement

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/leaf-properties-and-growth-measurement/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the development and validation of non-destructive methods for estimating leaf area in various plant species. The methods include digital image analysis, allometric models, linear measurements, and artificial neural networks. The research also covers the relationship between leaf area and plant growth, as well as phytochemical studies. |
| Domain | Life Sciences |
| Field | Agricultural and Biological Sciences |
| OpenAlex ID | t14365 |
| Works | 180 |

## Topic papers all

Showing 15 of 180.

- [A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition](https://scholariq.org/papers/a-robust-deep-learning-based-detector-for-real-time-tomato-plant-diseases-and/)
- [Using deep transfer learning for image-based plant disease identification](https://scholariq.org/papers/using-deep-transfer-learning-for-image-based-plant-disease-identification/)
- [Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation](https://scholariq.org/papers/estimating-chlorophyll-content-from-hyperspectral-vegetation-indices-modeling/)
- [Plant Disease Detection and Classification by Deep Learning](https://scholariq.org/papers/plant-disease-detection-and-classification-by-deep-learning/)
- [ToLeD: Tomato Leaf Disease Detection using Convolution Neural Network](https://scholariq.org/papers/toled-tomato-leaf-disease-detection-using-convolution-neural-network/)
- [An automated detection and classification of citrus plant diseases using image processing techniques: A review](https://scholariq.org/papers/an-automated-detection-and-classification-of-citrus-plant-diseases-using-image/)
- [Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives](https://scholariq.org/papers/review-of-indirect-optical-measurements-of-leaf-area-index-recent-advances/)
- [Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network](https://scholariq.org/papers/early-detection-and-classification-of-tomato-leaf-disease-using-high-performance/)
- [Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques](https://scholariq.org/papers/automatic-and-reliable-leaf-disease-detection-using-deep-learning-techniques/)
- [Non-destructive measurement of acidity, soluble solids and firmness of Satsuma mandarin using Vis/NIR-spectroscopy techniques](https://scholariq.org/papers/non-destructive-measurement-of-acidity-soluble-solids-and-firmness-of-satsuma/)
- [Fruit Quality Evaluation Using Spectroscopy Technology: A Review](https://scholariq.org/papers/fruit-quality-evaluation-using-spectroscopy-technology-a-review/)
- [Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging](https://scholariq.org/papers/identification-of-yellow-rust-in-wheat-using-in-situ-spectral-reflectance/)
- [Crop growth estimation system using machine vision](https://scholariq.org/papers/crop-growth-estimation-system-using-machine-vision/)
- [Rice Leaf Disease Detection Using Machine Learning Techniques](https://scholariq.org/papers/rice-leaf-disease-detection-using-machine-learning-techniques/)
- [Development of Efficient CNN model for Tomato crop disease identification](https://scholariq.org/papers/development-of-efficient-cnn-model-for-tomato-crop-disease-identification/)

## Topic primary papers

- [Crop growth estimation system using machine vision](https://scholariq.org/papers/crop-growth-estimation-system-using-machine-vision/)
- [From the Arctic to the tropics: multibiome prediction of leaf mass per area using leaf reflectance](https://scholariq.org/papers/from-the-arctic-to-the-tropics-multibiome-prediction-of-leaf-mass-per-area-using/)
- [Growth monitoring of greenhouse lettuce based on a convolutional neural network](https://scholariq.org/papers/growth-monitoring-of-greenhouse-lettuce-based-on-a-convolutional-neural-network/)
- [Landmark-free statistical analysis of the shape of plant leaves](https://scholariq.org/papers/landmark-free-statistical-analysis-of-the-shape-of-plant-leaves/)
- [An Analysis of Leaf Chlorophyll Measurement Method Using Chlorophyll Meter and Image Processing Technique](https://scholariq.org/papers/an-analysis-of-leaf-chlorophyll-measurement-method-using-chlorophyll-meter-and/)
- [Predicting foliar nutrient concentrations and nutrient deficiencies of hydroponic lettuce using hyperspectral imaging](https://scholariq.org/papers/predicting-foliar-nutrient-concentrations-and-nutrient-deficiencies-of/)
- [A decision support system for tobacco cultivation measures based on BPNN and GA](https://scholariq.org/papers/a-decision-support-system-for-tobacco-cultivation-measures-based-on-bpnn-and-ga/)
- [Comparison of Texture Based Feature Extraction Techniques for Detecting Leaf Scorch in Strawberry Plant (Fragaria × Ananassa)](https://scholariq.org/papers/comparison-of-texture-based-feature-extraction-techniques-for-detecting-leaf/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.