keyword
https://read.qxmd.com/read/38525140/sugarcane-breeding-a-fantastic-past-and-promising-future-driven-by-technology-and-methods
#1
JOURNAL ARTICLE
Guilong Lu, Purui Liu, Qibin Wu, Shuzhen Zhang, Peifang Zhao, Yuebin Zhang, Youxiong Que
Sugarcane is the most important sugar and energy crop in the world. During sugarcane breeding, technology is the requirement and methods are the means. As we know, seed is the cornerstone of the development of the sugarcane industry. Over the past century, with the advancement of technology and the expansion of methods, sugarcane breeding has continued to improve, and sugarcane production has realized a leaping growth, providing a large amount of essential sugar and clean energy for the long-term mankind development, especially in the face of the future threats of world population explosion, reduction of available arable land, and various biotic and abiotic stresses...
2024: Frontiers in Plant Science
https://read.qxmd.com/read/38524738/time-series-field-phenotyping-of-soybean-growth-analysis-by-combining-multimodal-deep-learning-and-dynamic-modeling
#2
JOURNAL ARTICLE
Hui Yu, Lin Weng, Songquan Wu, Jingjing He, Yilin Yuan, Jun Wang, Xiaogang Xu, Xianzhong Feng
The rate of soybean canopy establishment largely determines photoperiodic sensitivity, subsequently influencing yield potential. However, assessing the rate of soybean canopy development in large-scale field breeding trials is both laborious and time-consuming. High-throughput phenotyping methods based on unmanned aerial vehicle (UAV) systems can be used to monitor and quantitatively describe the development of soybean canopies for different genotypes. In this study, high-resolution and time-series raw data from field soybean populations were collected using UAVs...
2024: Plant phenomics: a science partner journal
https://read.qxmd.com/read/38516179/classification-of-wheat-diseases-using-deep-learning-networks-with-field-and-glasshouse-images
#3
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
#4
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
#5
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/38490467/asptf-a-computational-tool-to-predict-abiotic-stress-responsive-transcription-factors-in-plants-by-employing-machine-learning-algorithms
#6
JOURNAL ARTICLE
Upendra Kumar Pradhan, Anuradha Mahapatra, Sanchita Naha, Ajit Gupta, Rajender Parsad, Vijay Gahlaut, Surya Narayan Rath, Prabina Kumar Meher
BACKGROUND: Abiotic stresses pose serious threat to the growth and yield of crop plants. Several studies suggest that in plants, transcription factors (TFs) are important regulators of gene expression, especially when it comes to coping with abiotic stresses. Therefore, it is crucial to identify TFs associated with abiotic stress response for breeding of abiotic stress tolerant crop cultivars. METHODS: Based on a machine learning frame work, a computational model was envisaged to predict TFs associated with abiotic stress response in plants...
March 13, 2024: Biochimica et Biophysica Acta. General Subjects
https://read.qxmd.com/read/38481707/design-of-smart-citrus-picking-model-based-on-mask-rcnn-and-adaptive-threshold-segmentation
#7
JOURNAL ARTICLE
Ziwei Guo, Yuanwu Shi, Ibrar Ahmad
Smart agriculture is steadily progressing towards automation and heightened efficacy. The rapid ascent of deep learning technology provides a robust foundation for this trajectory. Leveraging computer vision and the depths of deep learning techniques enables real-time monitoring and management within agriculture, facilitating swift detection of plant growth and autonomous assessment of ripeness. In response to the demands of smart agriculture, this exposition delves into automated citrus harvesting, presenting an ATT-MRCNN target detection model that seamlessly integrates channel attention and spatial attention mechanisms for discerning and identifying citrus images...
2024: PeerJ. Computer Science
https://read.qxmd.com/read/38378736/ercp-net-a-channel-extension-residual-structure-and-adaptive-channel-attention-mechanism-for-plant-leaf-disease-classification-network
#8
JOURNAL ARTICLE
Xiu Ma, Wei Chen, Yannan Xu
Plant leaf diseases are a major cause of plant mortality, especially in crops. Timely and accurately identifying disease types and implementing proper treatment measures in the early stages of leaf diseases are crucial for healthy plant growth. Traditional plant disease identification methods rely heavily on visual inspection by experts in plant pathology, which is time-consuming and requires a high level of expertise. So, this approach fails to gain widespread adoption. To overcome these challenges, we propose a channel extension residual structure and adaptive channel attention mechanism for plant leaf disease classification network (ERCP-Net)...
February 20, 2024: Scientific Reports
https://read.qxmd.com/read/38360328/estimating-four-decadal-variations-of-seagrass-distribution-using-satellite-data-and-deep-learning-methods-in-a-marine-lagoon
#9
JOURNAL ARTICLE
Lulu Wang, Hanwei Liang, Shengqiang Wang, Deyong Sun, Junsheng Li, Hailong Zhang, Yibo Yuan
Seagrasses are marine flowering plants that inhabit shallow coastal and estuarine waters and serve vital ecological functions in marine ecosystems. However, seagrass ecosystems face the looming threat of degradation, necessitating effective monitoring. Remote-sensing technology offers significant advantages in terms of spatial coverage and temporal accessibility. Although some remote sensing approaches, such as water column correction, spectral index-based, and machine learning-based methods, have been proposed for seagrass detection, their performances are not always satisfactory...
February 13, 2024: Science of the Total Environment
https://read.qxmd.com/read/38323798/evaluation-of-two-deep-learning-based-approaches-for-detecting-weeds-growing-in-cabbage
#10
JOURNAL ARTICLE
Hu Sun, Teng Liu, Jinxu Wang, Danlan Zhai, Jialin Yu
BACKGROUND: Machine vision-based precision weed management is a promising solution to substantially reduce herbicide input and weed control cost. The objective of this research was to compare two different deep learning-based approaches for detecting weeds in cabbage: (1) detecting weeds directly, and (2) detecting crops by generating the bounding boxes covering the crops and any green pixels outside the bounding boxes were deemed as weeds. RESULTS: The precision, recall, F1-score, mAP0...
February 7, 2024: Pest Management Science
https://read.qxmd.com/read/38310270/a-hyperspectral-deep-learning-attention-model-for-predicting-lettuce-chlorophyll-content
#11
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/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/38146275/denoising-diffusion-probabilistic-models-and-transfer-learning-for-citrus-disease-diagnosis
#13
JOURNAL ARTICLE
Yuchen Li, Jianwen Guo, Honghua Qiu, Fengyi Chen, Junqi Zhang
PROBLEMS: Plant Disease diagnosis based on deep learning mechanisms has been extensively studied and applied. However, the complex and dynamic agricultural growth environment results in significant variations in the distribution of state samples, and the lack of sufficient real disease databases weakens the information carried by the samples, posing challenges for accurately training models. AIM: This paper aims to test the feasibility and effectiveness of Denoising Diffusion Probabilistic Models (DDPM), Swin Transformer model, and Transfer Learning in diagnosing citrus diseases with a small sample...
2023: Frontiers in Plant Science
https://read.qxmd.com/read/38132676/a-point-cloud-segmentation-network-based-on-squeezenet-and-time-series-for-plants
#14
JOURNAL ARTICLE
Xingshuo Peng, Keyuan Wang, Zelin Zhang, Nan Geng, Zhiyi Zhang
The phenotyping of plant growth enriches our understanding of intricate genetic characteristics, paving the way for advancements in modern breeding and precision agriculture. Within the domain of phenotyping, segmenting 3D point clouds of plant organs is the basis of extracting plant phenotypic parameters. In this study, we introduce a novel method for point-cloud downsampling that adeptly mitigates the challenges posed by sample imbalances. In subsequent developments, we architect a deep learning framework founded on the principles of SqueezeNet for the segmentation of plant point clouds...
November 23, 2023: Journal of Imaging
https://read.qxmd.com/read/38116146/research-on-weed-identification-in-soybean-fields-based-on-the-lightweight-segmentation-model-dcsanet
#15
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/38098093/ripening-dynamics-revisited-an-automated-method-to-track-the-development-of-asynchronous-berries-on-time-lapse-images
#16
JOURNAL ARTICLE
Benoit Daviet, Christian Fournier, Llorenç Cabrera-Bosquet, Thierry Simonneau, Maxence Cafier, Charles Romieu
BACKGROUND: Grapevine berries undergo asynchronous growth and ripening dynamics within the same bunch. Due to the lack of efficient methods to perform sequential non-destructive measurements on a representative number of individual berries, the genetic and environmental origins of this heterogeneity, remain nearly unknown. To address these limitations, we propose a method to track the growth and coloration kinetics of individual berries on time-lapse images of grapevine bunches. RESULTS: First, a deep-learning approach is used to detect berries with at least 50 ± 10% of visible contours, and infer the shape they would have in the absence of occlusions...
December 14, 2023: Plant Methods
https://read.qxmd.com/read/38093269/a-deep-learning-model-for-predicting-risks-of-crop-pests-and-diseases-from-sequential-environmental-data
#17
JOURNAL ARTICLE
Sangyeon Lee, Choa Mun Yun
Crop pests reduce productivity, so managing them through early detection and prevention is essential. Data from various modalities are being used to predict crop diseases by applying machine learning methodology. In particular, because growth environment data is relatively easy to obtain, many attempts are made to predict pests and diseases using it. In this paper, we propose a model that predicts diseases through previous growth environment information of crops, including air temperature, relative humidity, dew point, and CO2 concentration, using deep learning techniques...
December 14, 2023: Plant Methods
https://read.qxmd.com/read/38068677/comparison-of-different-machine-learning-algorithms-for-the-prediction-of-the-wheat-grain-filling-stage-using-rgb-images
#18
JOURNAL ARTICLE
Yunlin Song, Zhuangzhuang Sun, Ruinan Zhang, Haijiang Min, Qing Li, Jian Cai, Xiao Wang, Qin Zhou, Dong Jiang
UNLABELLED: Grain filling is essential for wheat yield formation, but is very susceptible to environmental stresses, such as high temperatures, especially in the context of global climate change. Grain RGB images include rich color, shape, and texture information, which can explicitly reveal the dynamics of grain filling. However, it is still challenging to further quantitatively predict the days after anthesis (DAA) from grain RGB images to monitor grain development. RESULTS: The WheatGrain dataset revealed dynamic changes in color, shape, and texture traits during grain development...
November 30, 2023: Plants (Basel, Switzerland)
https://read.qxmd.com/read/38068650/plant-physiological-analysis-to-overcome-limitations-to-plant-phenotyping
#19
REVIEW
Matthew Haworth, Giovanni Marino, Giulia Atzori, Andre Fabbri, Andre Daccache, Dilek Killi, Andrea Carli, Vincenzo Montesano, Adriano Conte, Raffaella Balestrini, Mauro Centritto
Plant physiological status is the interaction between the plant genome and the prevailing growth conditions. Accurate characterization of plant physiology is, therefore, fundamental to effective plant phenotyping studies; particularly those focused on identifying traits associated with improved yield, lower input requirements, and climate resilience. Here, we outline the approaches used to assess plant physiology and how these techniques of direct empirical observations of processes such as photosynthetic CO2 assimilation, stomatal conductance, photosystem II electron transport, or the effectiveness of protective energy dissipation mechanisms are unsuited to high-throughput phenotyping applications...
November 29, 2023: Plants (Basel, Switzerland)
https://read.qxmd.com/read/38053768/comparing-cnns-and-plsr-for-estimating-wheat-organs-biophysical-variables-using-proximal-sensing
#20
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
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