keyword
https://read.qxmd.com/read/38516179/classification-of-wheat-diseases-using-deep-learning-networks-with-field-and-glasshouse-images
#1
JOURNAL ARTICLE
Megan Long, Matthew Hartley, Richard J Morris, James K M Brown
Crop diseases can cause major yield losses, so the ability to detect and identify them in their early stages is important for disease control. Deep learning methods have shown promise in classifying multiple diseases; however, many studies do not use datasets that represent real field conditions, necessitating either further image processing or reducing their applicability. In this paper, we present a dataset of wheat images taken in real growth situations, including both field and glasshouse conditions, with five categories: healthy plants and four foliar diseases, yellow rust, brown rust, powdery mildew and Septoria leaf blotch...
April 2023: Plant Pathology
https://read.qxmd.com/read/38493119/aradq-an-automated-digital-phenotyping-software-for-quantifying-disease-symptoms-of-flood-inoculated-arabidopsis-seedlings
#2
JOURNAL ARTICLE
Jae Hoon Lee, Unseok Lee, Ji Hye Yoo, Taek Sung Lee, Je Hyeong Jung, Hyoung Seok Kim
BACKGROUND: Plant scientists have largely relied on pathogen growth assays and/or transcript analysis of stress-responsive genes for quantification of disease severity and susceptibility. These methods are destructive to plants, labor-intensive, and time-consuming, thereby limiting their application in real-time, large-scale studies. Image-based plant phenotyping is an alternative approach that enables automated measurement of various symptoms. However, most of the currently available plant image analysis tools require specific hardware platform and vendor specific software packages, and thus, are not suited for researchers who are not primarily focused on plant phenotyping...
March 16, 2024: Plant Methods
https://read.qxmd.com/read/38491134/empowering-coffee-farming-using-counterfactual-recommendation-based-rnn-driven-iot-integrated-soil-quality-command-system
#3
JOURNAL ARTICLE
Raveena Selvanarayanan, Surendran Rajendran, Sameer Algburi, Osamah Ibrahim Khalaf, Habib Hamam
Soil health is essential for whirling stale soil into rich coffee-growing land. By keeping healthy soil, coffee producers may improve plant growth, leaf health, buds, cherry and bean quality, and yield. Traditional soil monitoring is tedious, time-consuming, and error-prone. Enhancing the monitoring system using AI-based IoT technologies for quick and precise changes. Integrated soil fertility control system to optimize soil health, maximize efficiency, promote sustainability, and prevent crop threads using real-time data analysis to turn infertile land into fertile land...
March 15, 2024: Scientific Reports
https://read.qxmd.com/read/38337925/high-throughput-analysis-of-leaf-chlorophyll-content-in-aquaponically-grown-lettuce-using-hyperspectral-reflectance-and-rgb-images
#4
JOURNAL ARTICLE
Mohamed Farag Taha, Hanping Mao, Yafei Wang, Ahmed Islam ElManawy, Gamal Elmasry, Letian Wu, Muhammad Sohail Memon, Ziang Niu, Ting Huang, Zhengjun Qiu
Chlorophyll content reflects plants' photosynthetic capacity, growth stage, and nitrogen status and is, therefore, of significant importance in precision agriculture. This study aims to develop a spectral and color vegetation indices-based model to estimate the chlorophyll content in aquaponically grown lettuce. A completely open-source automated machine learning (AutoML) framework (EvalML) was employed to develop the prediction models. The performance of AutoML along with four other standard machine learning models (back-propagation neural network (BPNN), partial least squares regression (PLSR), random forest (RF), and support vector machine (SVM) was compared...
January 29, 2024: Plants (Basel, Switzerland)
https://read.qxmd.com/read/38310270/a-hyperspectral-deep-learning-attention-model-for-predicting-lettuce-chlorophyll-content
#5
JOURNAL ARTICLE
Ziran Ye, Xiangfeng Tan, Mengdi Dai, Xuting Chen, Yuanxiang Zhong, Yi Zhang, Yunjie Ruan, Dedong Kong
BACKGROUND: The phenotypic traits of leaves are the direct reflection of the agronomic traits in the growth process of leafy vegetables, which plays a vital role in the selection of high-quality leafy vegetable varieties. The current image-based phenotypic traits extraction research mainly focuses on the morphological and structural traits of plants or leaves, and there are few studies on the phenotypes of physiological traits of leaves. The current research has developed a deep learning model aimed at predicting the total chlorophyll of greenhouse lettuce directly from the full spectrum of hyperspectral images...
February 3, 2024: Plant Methods
https://read.qxmd.com/read/38298139/measuring-stomatal-and-guard-cell-metrics-for-plant-physiology-and-growth-using-stomanager1
#6
JOURNAL ARTICLE
Jiaxin Wang, Heidi J Renninger, Qin Ma, Shichao Jin
Automated guard cell detection and measurement are vital for understanding plant physiological performance and ecological functioning in global water and carbon cycles. Most current methods for measuring guard cells and stomata are laborious, time-consuming, prone to bias, and limited in scale. We developed StoManager1, a high-throughput tool utilizing geometrical and mathematical algorithms and convolutional neural networks to automatically detect, count, and measure over 30 guard cell and stomatal metrics, including guard cell and stomatal area, length, width, stomatal aperture area/guard cell area, orientation, stomatal evenness, divergence, and aggregation index...
January 31, 2024: Plant Physiology
https://read.qxmd.com/read/38295640/assessing-greenhouse-gas-emissions-in-cuban-agricultural-soils-implications-for-climate-change-and-rice-oryza-sativa-l-production
#7
JOURNAL ARTICLE
Afzal Ahmed Dar, Zhi Chen, Sergio Rodríguez-Rodríguez, Fariborz Haghighat, Beatriz González-Rosales
Assessing the impact of greenhouse gas (GHG) emissions on agricultural soils is crucial for ensuring food production sustainability in the global effort to combat climate change. The present study delves to comprehensively assess GHG emissions in Cuba's agricultural soil and analyze its implications for rice production and climate change because of its rich agriculture cultivation tradition and diverse agro-ecological zones from the period of 1990-2022. In this research, based on Autoregressive Distributed Lag (ARDL) approach the empirical findings depicts that in short run, a positive and significant impact of 1...
January 30, 2024: Journal of Environmental Management
https://read.qxmd.com/read/38263036/predicting-and-optimizing-reactive-oxygen-species-metabolism-in-punica-granatum-l-through-machine-learning-role-of-exogenous-gaba-on-antioxidant-enzyme-activity-under-drought-and-salinity-stress
#8
JOURNAL ARTICLE
Saeedeh Zarbakhsh, Ali Reza Shahsavar, Ali Afaghi, Mirza Hasanuzzaman
BACKGROUND: Drought and salinity stress have been proposed as the main environmental factors threatening food security, as they adversely affect crops' agricultural productivity. As a potential solution, the application of plant growth regulators to enhance drought and salinity tolerance has gained considerable attention. γ-aminobutyric acid (GABA) is a four-carbon non-protein amino acid that accumulates in plants as a response to stressful conditions. This study focused on a comparative assessment of several machine learning (ML) regression models, including radial basis function, generalized regression neural network (GRNN), random forest (RF), and support vector regression (SVR) to develop predictive models for assessing the effect of different concentrations of GABA (0, 10, 20, and 40 mM) on various physio-biochemical traits during periods of drought, salinity, and combined stress conditions...
January 23, 2024: BMC Plant Biology
https://read.qxmd.com/read/38233479/a-cnn-based-model-to-count-the-leaves-of-rosette-plants-lc-net
#9
JOURNAL ARTICLE
Mainak Deb, Krishna Gopal Dhal, Arunita Das, Abdelazim G Hussien, Laith Abualigah, Arpan Garai
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 component of plant phenotyping, which can be used to measure the growth of the plant. Therefore, this paper developed a convolutional neural network-based leaf counting model called LC-Net. The original plant image and segmented leaf parts are fed as input because the segmented leaf part provides additional information to the proposed LC-Net...
January 17, 2024: Scientific Reports
https://read.qxmd.com/read/38230354/automatic-root-length-estimation-from-images-acquired-in%C3%A2-situ-without-segmentation
#10
JOURNAL ARTICLE
Faina Khoroshevsky, Kaining Zhou, Sharon Chemweno, Yael Edan, Aharon Bar-Hillel, Ofer Hadar, Boris Rewald, Pavel Baykalov, Jhonathan E Ephrath, Naftali Lazarovitch
Image-based root phenotyping technologies, including the minirhizotron (MR), have expanded our understanding of the in situ root responses to changing environmental conditions. The conventional manual methods used to analyze MR images are time-consuming, limiting their implementation. This study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total (cumulative) root length (TRL) per MR image without requiring segmentation. Training data were derived from manual annotations in Rootfly, commonly used software for MR image analysis...
2024: Plant phenomics: a science partner journal
https://read.qxmd.com/read/38202447/monitoring-of-nitrogen-concentration-in-soybean-leaves-at-multiple-spatial-vertical-scales-based-on-spectral-parameters
#11
JOURNAL ARTICLE
Tao Sun, Zhijun Li, Zhangkai Wang, Yuchen Liu, Zhiheng Zhu, Yizheng Zhao, Weihao Xie, Shihao Cui, Guofu Chen, Wanli Yang, Zhitao Zhang, Fucang Zhang
Nitrogen is a fundamental component for building amino acids and proteins, playing a crucial role in the growth and development of plants. Leaf nitrogen concentration (LNC) serves as a key indicator for assessing plant growth and development. Monitoring LNC provides insights into the absorption and utilization of nitrogen from the soil, offering valuable information for rational nutrient management. This, in turn, contributes to optimizing nutrient supply, enhancing crop yields, and minimizing adverse environmental impacts...
January 4, 2024: Plants (Basel, Switzerland)
https://read.qxmd.com/read/38190348/plantc2u-deep-learning-of-cross-species-sequence-landscapes-predict-plastid-c-to-u-rna-editing-in-plants
#12
JOURNAL ARTICLE
Chaoqun Xu, Jing Li, Ling-Yu Song, Ze-Jun Guo, Shi-Wei Song, Lu-Dan Zhang, Hai-Lei Zheng
In plants, C-to-U RNA editing is mainly occurred in the plastids and mitochondria transcripts, which contributes to complex transcriptional regulatory network. More evidences reveal that RNA editing plays critical roles in plant growth and development. However, RNA editing sites accurately detected by transcriptome sequencing data alone are still challenging. In the present study, we develop PlantC2U, which is a convolutional neural network to predict plastid C-to-U RNA editing based on the genomic sequence...
January 8, 2024: Journal of Experimental Botany
https://read.qxmd.com/read/38116146/research-on-weed-identification-in-soybean-fields-based-on-the-lightweight-segmentation-model-dcsanet
#13
JOURNAL ARTICLE
Helong Yu, Minghang Che, Han Yu, Yuntao Ma
Weeds can compete with crops for sunlight, water, space and various nutrients, which can affect the growth of crops.In recent years, people have started to use self-driving agricultural equipment, robots, etc. for weeding work and use of drones for weed identification and spraying of weeds with herbicides, and the effectiveness of these mobile weeding devices is largely limited by the superiority of weed detection capability. To improve the weed detection capability of mobile weed control devices, this paper proposes a lightweight weed segmentation network model DCSAnet that can be better applied to mobile weed control devices...
2023: Frontiers in Plant Science
https://read.qxmd.com/read/38053768/comparing-cnns-and-plsr-for-estimating-wheat-organs-biophysical-variables-using-proximal-sensing
#14
JOURNAL ARTICLE
Alexis Carlier, Sébastien Dandrifosse, Benjamin Dumont, Benoit Mercatoris
Estimation of biophysical vegetation variables is of interest for diverse applications, such as monitoring of crop growth and health or yield prediction. However, remote estimation of these variables remains challenging due to the inherent complexity of plant architecture, biology and surrounding environment, and the need for features engineering. Recent advancements in deep learning, particularly convolutional neural networks (CNN), offer promising solutions to address this challenge. Unfortunately, the limited availability of labeled data has hindered the exploration of CNNs for regression tasks, especially in the frame of crop phenotyping...
2023: Frontiers in Plant Science
https://read.qxmd.com/read/38023901/convolutional-neural-network-in-rice-disease-recognition-accuracy-speed-and-lightweight
#15
REVIEW
Hongwei Ning, Sheng Liu, Qifei Zhu, Teng Zhou
There are many rice diseases, which have very serious negative effects on rice growth and final yield. It is very important to identify the categories of rice diseases and control them. In the past, the identification of rice disease types was completely dependent on manual work, which required a high level of human experience. But the method often could not achieve the desired effect, and was difficult to popularize on a large scale. Convolutional neural networks are good at extracting localized features from input data, converting low-level shape and texture features into high-level semantic features...
2023: Frontiers in Plant Science
https://read.qxmd.com/read/38023863/osc-co-2-coattention-and-cosegmentation-framework-for-plant-state-change-with-multiple-features
#16
JOURNAL ARTICLE
Rubi Quiñones, Ashok Samal, Sruti Das Choudhury, Francisco Muñoz-Arriola
Cosegmentation and coattention are extensions of traditional segmentation methods aimed at detecting a common object (or objects) in a group of images. Current cosegmentation and coattention methods are ineffective for objects, such as plants, that change their morphological state while being captured in different modalities and views. The Object State Change using Coattention-Cosegmentation (OSC-CO2 ) is an end-to-end unsupervised deep-learning framework that enhances traditional segmentation techniques, processing, analyzing, selecting, and combining suitable segmentation results that may contain most of our target object's pixels, and then displaying a final segmented image...
2023: Frontiers in Plant Science
https://read.qxmd.com/read/38005764/hierarchical-machine-learning-based-growth-prediction-model-of-panax-ginseng-sprouts-in-a-hydroponic-environment
#17
JOURNAL ARTICLE
Tae Hyong Kim, Seunghoon Baek, Ki Hyun Kwon, Seung Eel Oh
Due to an increase in interest towards functional and health-related foods, Panax ginseng sprout has been in the spotlight since it contains a significant amount of saponins which have anti-cancer, -stress, and -diabetic effects. To increase the amount of production as well as decrease the cultivation period, sprouted ginseng is being studied to ascertain its optimal cultivation environment in hydroponics. Although there are studies on functional components, there is a lack of research on early disease prediction along with productivity improvement...
November 15, 2023: Plants (Basel, Switzerland)
https://read.qxmd.com/read/37913659/screening-the-phytotoxicity-of-micro-nanoplastics-through-non-targeted-metallomics-with-synchrotron-radiation-x-ray-fluorescence-and-deep-learning-taking-micro-nano-polyethylene-terephthalate-as-an-example
#18
JOURNAL ARTICLE
Hongxin Xie, Chaojie Wei, Wei Wang, Rui Chen, Liwei Cui, Liming Wang, Dongliang Chen, Yong-Liang Yu, Bai Li, Yu-Feng Li
Microplastics (MPs) and nanoplastics (NPs) are global pollutants with emerging concerns. Methods to predict and screen their toxicity are crucial. Elemental dyshomeostasis can be used to assess toxicity of environmental pollutants. Non-targeted metallomics, combining synchrotron radiation X-ray fluorescence (SRXRF) and machine learning, has successfully differentiated cancer patients from healthy individuals. The whole idea of this work is to screen the phytotoxicity of nano polyethylene terephthalate (nPET) and micro polyethylene terephthalate (mPET) through non-targeted metallomics with SRXRF and deep learning algorithms...
October 28, 2023: Journal of Hazardous Materials
https://read.qxmd.com/read/37908836/modeling-the-spatial-spectral-characteristics-of-plants-for-nutrient-status-identification-using-hyperspectral-data-and-deep-learning-methods
#19
JOURNAL ARTICLE
Frank Gyan Okyere, Daniel Cudjoe, Pouria Sadeghi-Tehran, Nicolas Virlet, Andrew B Riche, March Castle, Latifa Greche, Daniel Simms, Manal Mhada, Fady Mohareb, Malcolm John Hawkesford
Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants...
2023: Frontiers in Plant Science
https://read.qxmd.com/read/37900733/research-on-the-evolutionary-history-of-the-morphological-structure-of-cotton-seeds-a-new-perspective-based-on-high-resolution-micro-ct-technology
#20
JOURNAL ARTICLE
Yuankun Li, Guanmin Huang, Xianju Lu, Shenghao Gu, Ying Zhang, Dazhuang Li, Minkun Guo, Yongjiang Zhang, Xinyu Guo
Cotton ( Gossypium hirsutum L.) seed morphological structure has a significant impact on the germination, growth and quality formation. However, the wide variation of cotton seed morphology makes it difficult to achieve quantitative analysis using traditional phenotype acquisition methods. In recent years, the application of micro-CT technology has made it possible to analyze the three-dimensional morphological structure of seeds, and has shown technical advantages in accurate identification of seed phenotypes...
2023: Frontiers in Plant Science
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