Plant Methods

2024

Deep Learning & Transfer Learning: CNNs for Plant Disease Detection, Classification, and Technological Innovation
The advancement of deep learning and transfer learning techniques, particularly convolutional neural networks (CNNs), has revolutionized the field of plant disease detection and classification. These innovations have enabled the development of sophisticated image processing algorithms that can accurately identify and classify plant diseases, leading to improved crop management and agricultural sustainability.

2023

Bioactive Natural Products from Plants: Analysis and Association with Human Health
Plants are rich in various bioactive molecules, including polyphenols, carotenoids, phytosterols, tocopherols, tocotrienols, organosulfur compounds, peptides, and fibers. These bioactive compounds have been proven to promote human health by preventing the onset and progression of diseases.

Development of New Sensing Technology in Sustainable Farming and Smart Environmental Monitoring
In recent years, new sensing technology and instruments, artificial intelligence, big data and Internet of Things have developed rapidly. The application of such technology and instruments has permeated almost all scientific research and production practices in all walks of life. Not unexpectedly, it has already had a profound impact on plant science. 

Speed Breeding in Crops
In the scenario of global climate change, food security is a critical issue due to the increasing human population and environmental pollutants, and one of the greatest challenges is how to accelerate plant breeding programs for future crops with high yield and stress tolerance.

2022

Accelerating image-based plant phenotyping and pattern recognition: deep learning or few-shot learning?
In recent years, deep learning methods have played a great role in the plant sciences and achieved a series of remarkable achievements in many fields. Few-shot learning is a new branch of deep learning, which aims to develop an intelligent model with good generalization from only few data, towards the combination of machine intelligence with flexibility and extensibility. Both deep learning and few-shot learning are technological explorations in the field of plant sciences that have the potential to greatly accelerate their applications.

2021

Deep learning or few-shot learning?
Few-shot learning is a new branch of deep learning, which aims to develop an intelligent model with good generalization from only few data, towards the combination of machine intelligence with flexibility and extensibility. Both deep learning and few-shot learning are technological explorations in the field of plant sciences that have the potential to greatly accelerate their applications. 

2018

Plants in computer vision
This collection includes work on plant detection, segmentation and modelling from image data, at many different scales.

2016

Plant genome editing
This series focuses on gene editing as an effective tool in plant biotechnology and genetic engineering. 

2014

Plant phenotyping and phenomics

This series covers all aspects of phenotyping and phenomics technologies as applied to plant research and includes and includes a number of invited contributions from speakers at the 3rd International Plant Phenotyping Symposium.

2013

Next Generation Sequencing technologies for plant research

This series focuses on the use of different sequencing technologies that allow us to sequence DNA and RNA much more quickly and cheaply than Sanger sequencing and their application to plant research.