Plant phenotyping has been recognized as a bottleneck for improving the efficiency of breeding programs, understanding plant-environment interactions, and managing agricultural systems. In the past five years, imaging approaches have shown great potential for high-throughput plant phenotyping, resulting in more attention paid to imaging-based plant phenotyping. With this increased amount of image data, it has become urgent to develop robust analytical tools that can extract phenotypic traits accurately and rapidly. The goal of this review is to provide a comprehensive overview of the latest studies using deep convolutional neural networks (CNNs) in plant phenotyping applications. We specifically review the use of various CNN architecture for plant stress evaluation, plant development, and postharvest quality assessment. We systematically organize the studies based on technical developments resulting from imaging classification, object detection, and image segmentation, thereby identifying state-of-the-art solutions for certain phenotyping applications. Finally, we provide several directions for future research in the use of CNN architecture for plant phenotyping purposes.

/pdf/convolutional-neural-networks-for-image-based-high-3civyc1i7o.pdf

Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review.

On line detection of defective apples using computer vision system combined with deep learning methods

Measurement of optical properties of fruits and vegetables: A review

Real-time defects detection for apple sorting using NIR cameras with pruning-based YOLOV4 network

Background Bruising is the most common type of mechanical damage, which is inevitable along the postharvest chain, including harvesting, sorting, packaging and so on. It's the major cause of postharvest loss. However, the detection of bruises mainly relies on manual inspection which is time-consuming and mistake-prone, especially for the early bruises. In recent years, with the development of computer vision, more and more imaging techniques for real-time and automated bruise detection are proposed and studied again. Scope and approach This review provides an overview of recent advances in imaging techniques for bruise detection. The imaging techniques presented include spectral techniques (biospeckle imaging, hyperspectral/multispectral imaging, fluorescence imaging and structured-illumination reflectance imaging), nuclear magnetic techniques (magnetic resonance imaging and X-ray imaging) and thermal imaging. The basic principles of these imaging techniques and their application in bruise detection are concisely introduced. Key findings and conclusion The imaging techniques mentioned above exhibit potential to detect the bruised tissue and the bruise volume in real time and to overcome the fallibility, tediousness, destructiveness, and time-consumption, which are the disadvantages of traditional methods. In future research, integration of these techniques, developing specific algorithms for bruise detection and attempting to apply them into an assembly line for commercial value will be the research hotpots. In addition, the range of applications of these techniques for fruit and vegetable types also needs to be extended, and applying them in a practically assembly line should be enhanced.

Recent advances in imaging techniques for bruise detection in fruits and vegetables

Detection of early decay in peaches by structured-illumination reflectance imaging

The invention relates to a gender determination method for a chicken hatching egg incubation early embryo based on a hyperspectral image, and belongs to the non-destructive testing technology in the poultry propagation industry. The gender determination method comprises the following steps: utilizing a hyperspectral imager to obtain a transmission hyperspectral image on the tenth day of chicken embryo hatching, analyzing difference of spectral response between a male embryo and a female embryo, extracting a spectral value within a wavelength range of 600-900 nm as an input value of a neural network, and judging the gender of the chicken embryo. With adoption of the method, the gender of the chicken embryo can be accurately determined, manual detection after hatching is replaced, the cost in the poultry production industry is lowered, and the production efficiency is improved.

Gender determination method for chicken hatching egg incubation early embryo based on hyperspectral image

The invention relates to a method for identifying hollowness of white radishes based on a hyperspectral image, belonging to the nondestructive testing technology in agricultural product storage and processing industry. The method comprises the following steps: acquiring a transmitting hyperspectral image in the white radish storage process by virtue of a hyperspectral imager, analyzing the difference of spectral responses of normal white radishes and hollowed white radishes, extracting spectral values in the wavelength of 400-1000nm to serve as input values of a neural network, and judging whether the white radishes are hollowed. According to the method, the hollowness of the white radishes can be accurately identified, the manual destructive detection is replaced, unqualified products are effectively prevented from flowing into the market, the eating and processing utilization rate of the white radishes is improved, the deep processing industrial development of white radishes is facilitated, and a reference is provided for applying the hyperspectral technology to the field of agricultural products.

Method for identifying hollowness of white radishes based on hyperspectral image

The invention relates to a method for non-destructive identification and distinction of paddy moldy fungal colony based on computer vision technique, and belongs to a novel fungi identification technology. The method includes acquiring an image of fungal colony with a computer vision device, extracting the image parameters after processing the image of fungal colony, optimizing the characteristic parameters, performing analysis of the different fungal colony characteristics by using a mode recognition technique, and reaching identification and distinction of fungi. The method can realize the accurate identification of the fungi spieces, and is rapider, more accurate and convenient than traditional artificial morphology identification and biological identification. The method provides a novel method for fungi identification, and has very important meaning for preventing and controlling the paddy from mildewing.

Method for non-destructive identification of paddy moldy fungal colony

The invention relates to a method for producing selenium-enriched protein powder by treating selenium-enriched rice in an enzymatic method and belongs to the field of food processing. The method is used for preparing selenium-enriched protein by treating selenium-enriched rice with alkaline protease. According to the method, the optimum condition of extract of the selenium-enriched protein by using alkaline protease is determined by researching the influence of temperature, pH, liquid-solid ratio, enzyme addition amount and time on the extraction rate of the selenium-enriched protein and an orthogonal test, the extraction rate of the selenium-enriched protein is 69.0%, and in the obtained protein powder, the purity of the protein is 91.1% and the content of selenium is 0.529mg/kg.

Method for producing selenium-enriched protein powder by treating selenium-enriched rice in enzymatic method

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Sun Ye

Author Tools

Chat about Author