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Kim Y, Abebe AM, Kim J, Hong S, An K, Shim J, Baek J. Deep learning-based elaiosome detection in milk thistle seed for efficient high-throughput phenotyping. FRONTIERS IN PLANT SCIENCE 2024; 15:1395558. [PMID: 39129764 PMCID: PMC11310567 DOI: 10.3389/fpls.2024.1395558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 07/04/2024] [Indexed: 08/13/2024]
Abstract
Milk thistle, Silybum marianum (L.), is a well-known medicinal plant used for the treatment of liver diseases due to its high content of silymarin. The seeds contain elaiosome, a fleshy structure attached to the seeds, which is believed to be a rich source of many metabolites including silymarin. Segmentation of elaiosomes using only image analysis is difficult, and this makes it impossible to quantify the elaiosome phenotypes. This study proposes a new approach for semi-automated detection and segmentation of elaiosomes in milk thistle seed using the Detectron2 deep learning algorithm. One hundred manually labeled images were used to train the initial elaiosome detection model. This model was used to predict elaiosome from new datasets, and the precise predictions were manually selected and used as new labeled images for retraining the model. Such semi-automatic image labeling, i.e., using the prediction results of the previous stage for retraining the model, allowed the production of sufficient labeled data for retraining. Finally, a total of 6,000 labeled images were used to train Detectron2 for elaiosome detection and attained a promising result. The results demonstrate the effectiveness of Detectron2 in detecting milk thistle seed elaiosomes with an accuracy of 99.9%. The proposed method automatically detects and segments elaiosome from the milk thistle seed. The predicted mask images of elaiosome were used to analyze its area as one of the seed phenotypic traits along with other seed morphological traits by image-based high-throughput phenotyping in ImageJ. Enabling high-throughput phenotyping of elaiosome and other seed morphological traits will be useful for breeding milk thistle cultivars with desirable traits.
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Affiliation(s)
- Younguk Kim
- Gene Engineering Division, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju, Republic of Korea
| | - Alebel Mekuriaw Abebe
- Gene Engineering Division, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju, Republic of Korea
| | - Jaeyoung Kim
- Gene Engineering Division, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju, Republic of Korea
| | - Suyoung Hong
- Genomics Division, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju, Republic of Korea
| | | | | | - Jeongho Baek
- Gene Engineering Division, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju, Republic of Korea
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2
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Wang Y, Ye F, Zeng J, Cai J, Huang W. Adherent Peanut Image Segmentation Based on Multi-Modal Fusion. SENSORS (BASEL, SWITZERLAND) 2024; 24:4434. [PMID: 39065831 PMCID: PMC11281241 DOI: 10.3390/s24144434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 06/25/2024] [Accepted: 06/26/2024] [Indexed: 07/28/2024]
Abstract
Aiming at the problem of the difficult segmentation of adherent images due to the not fully convex shape of peanut pods, their complex surface texture, and their diverse structures, a multimodal fusion algorithm is proposed to achieve a 2D segmentation of adherent peanut images with the assistance of 3D point clouds. Firstly, the point cloud of a running peanut is captured line by line using a line structured light imaging system, and its three-dimensional shape is obtained through splicing and combining it with a local surface-fitting algorithm to calculate a normal vector and curvature. Seed points are selected based on the principle of minimum curvature, and neighboring points are searched using the KD-Tree algorithm. The point cloud is filtered and segmented according to the normal angle and the curvature threshold until achieving the completion of the point cloud segmentation of the individual peanut, and then the two-dimensional contour of the individual peanut model is extracted by using the rolling method. The search template is established, multiscale feature matching is implemented on the adherent image to achieve the region localization, and finally, the segmentation region is optimized by an opening operation. The experimental results show that the algorithm improves the segmentation accuracy, and the segmentation accuracy reaches 96.8%.
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Affiliation(s)
- Yujing Wang
- College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China; (Y.W.); (J.C.)
| | - Fang Ye
- College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China; (Y.W.); (J.C.)
| | - Jiusun Zeng
- School of Mathematics, Hangzhou Normal University, Hangzhou 311121, China;
| | - Jinhui Cai
- College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China; (Y.W.); (J.C.)
| | - Wangsen Huang
- Wenzhou Quality and Technology Testing Research Institute, Wenzhou 325052, China;
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3
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He J, Weng L, Xu X, Chen R, Peng B, Li N, Xie Z, Sun L, Han Q, He P, Wang F, Yu H, Bhat JA, Feng X. DEKR-SPrior: An Efficient Bottom-Up Keypoint Detection Model for Accurate Pod Phenotyping in Soybean. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0198. [PMID: 38939747 PMCID: PMC11209727 DOI: 10.34133/plantphenomics.0198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 05/16/2024] [Indexed: 06/29/2024]
Abstract
The pod and seed counts are important yield-related traits in soybean. High-precision soybean breeders face the major challenge of accurately phenotyping the number of pods and seeds in a high-throughput manner. Recent advances in artificial intelligence, especially deep learning (DL) models, have provided new avenues for high-throughput phenotyping of crop traits with increased precision. However, the available DL models are less effective for phenotyping pods that are densely packed and overlap in in situ soybean plants; thus, accurate phenotyping of the number of pods and seeds in soybean plant is an important challenge. To address this challenge, the present study proposed a bottom-up model, DEKR-SPrior (disentangled keypoint regression with structural prior), for in situ soybean pod phenotyping, which considers soybean pods and seeds analogous to human people and joints, respectively. In particular, we designed a novel structural prior (SPrior) module that utilizes cosine similarity to improve feature discrimination, which is important for differentiating closely located seeds from highly similar seeds. To further enhance the accuracy of pod location, we cropped full-sized images into smaller and high-resolution subimages for analysis. The results on our image datasets revealed that DEKR-SPrior outperformed multiple bottom-up models, viz., Lightweight-OpenPose, OpenPose, HigherHRNet, and DEKR, reducing the mean absolute error from 25.81 (in the original DEKR) to 21.11 (in the DEKR-SPrior) in pod phenotyping. This paper demonstrated the great potential of DEKR-SPrior for plant phenotyping, and we hope that DEKR-SPrior will help future plant phenotyping.
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Affiliation(s)
- Jingjing He
- Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China
| | - Lin Weng
- Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China
| | - Xiaogang Xu
- School of Computer Science and Technology,
Zhejiang Gongshang University, Hangzhou 310012, Zhejiang, China
| | - Ruochen Chen
- Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China
| | - Bo Peng
- Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China
| | - Nannan Li
- Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China
| | - Zhengchao Xie
- Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China
| | - Lijian Sun
- Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China
| | - Qiang Han
- Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China
| | - Pengfei He
- Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China
| | - Fangfang Wang
- Zhejiang Laboratory, Hangzhou 311100, Zhejiang, China
| | - Hui Yu
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology,
Chinese Academy of Sciences, Changchun 130102, Jilin, China
| | | | - Xianzhong Feng
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology,
Chinese Academy of Sciences, Changchun 130102, Jilin, China
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4
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Duc NT, Ramlal A, Rajendran A, Raju D, Lal SK, Kumar S, Sahoo RN, Chinnusamy V. Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean. FRONTIERS IN PLANT SCIENCE 2023; 14:1206357. [PMID: 37771485 PMCID: PMC10523016 DOI: 10.3389/fpls.2023.1206357] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 07/26/2023] [Indexed: 09/30/2023]
Abstract
Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs. The present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean. The image-based seed architectural traits (i-traits) measured were area size (AS), perimeter length (PL), length (L), width (W), length-to-width ratio (LWR), intersection of length and width (IS), seed circularity (CS), and distance between IS and CG (DS). The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually measured seed weight. Seven popular machine learning (ML) algorithms, namely Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), LASSO Regression (LR), Ridge Regression (RR), and Elastic Net Regression (EN), were used to create models that can predict the weight of soybean seeds based on the image-based novel features derived from the Red-Green-Blue (RGB)/visual image. Among the models, random forest and multiple linear regression models that use multiple explanatory variables related to seed size traits (AS, L, W, and DS) were identified as the best models for predicting seed weight with the highest prediction accuracy (coefficient of determination, R2=0.98 and 0.94, respectively) and the lowest prediction error, i.e., root mean square error (RMSE) and mean absolute error (MAE). Finally, principal components analysis (PCA) and a hierarchical clustering approach were used to identify IC538070 as a superior genotype with a larger seed size and weight. The identified donors/traits can potentially be used in soybean improvement programs.
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Affiliation(s)
- Nguyen Trung Duc
- Division of Plant Physiology, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
- Vietnam National University of Agriculture, Hanoi, Vietnam
| | - Ayyagari Ramlal
- Division of Genetics, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
- School of Biological Sciences, Universiti Sains Malaysia (USM), Georgetown, Penang, Malaysia
| | - Ambika Rajendran
- Division of Genetics, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
| | - Dhandapani Raju
- Division of Plant Physiology, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
| | - S. K. Lal
- Division of Genetics, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
| | - Sudhir Kumar
- Division of Plant Physiology, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
| | - Rabi Narayan Sahoo
- Division of Agricultural Physics, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
| | - Viswanathan Chinnusamy
- Division of Plant Physiology, Indian Council of Agricultural Research-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India
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Ghimire A, Kim SH, Cho A, Jang N, Ahn S, Islam MS, Mansoor S, Chung YS, Kim Y. Automatic Evaluation of Soybean Seed Traits Using RGB Image Data and a Python Algorithm. PLANTS (BASEL, SWITZERLAND) 2023; 12:3078. [PMID: 37687325 PMCID: PMC10490075 DOI: 10.3390/plants12173078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 08/11/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023]
Abstract
Soybean (Glycine max) is a crucial legume crop known for its nutritional value, as its seeds provide large amounts of plant protein and oil. To ensure maximum productivity in soybean farming, it is essential to carefully choose high-quality seeds that possess desirable characteristics, such as the appropriate size, shape, color, and absence of any damage. By studying the relationship between seed shape and other traits, we can effectively identify different genotypes and improve breeding strategies to develop high-yielding soybean seeds. This study focused on the analysis of seed traits using a Python algorithm. The seed length, width, projected area, and aspect ratio were measured, and the total number of seeds was calculated. The OpenCV library along with the contour detection function were used to measure the seed traits. The seed traits obtained through the algorithm were compared with the values obtained manually and from two software applications (SmartGrain and WinDIAS). The algorithm-derived measurements for the seed length, width, and projected area showed a strong correlation with the measurements obtained using various methods, with R-square values greater than 0.95 (p < 0.0001). Similarly, the error metrics, including the residual standard error, root mean square error, and mean absolute error, were all below 0.5% when comparing the seed length, width, and aspect ratio across different measurement methods. For the projected area, the error was less than 4% when compared with different measurement methods. Furthermore, the algorithm used to count the number of seeds present in the acquired images was highly accurate, and only a few errors were observed. This was a preliminary study that investigated only some morphological traits, and further research is needed to explore more seed attributes.
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Affiliation(s)
- Amit Ghimire
- Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea; (A.G.); (M.S.I.)
| | - Seong-Hoon Kim
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, RDA, Jeonju 5487, Republic of Korea;
| | - Areum Cho
- School of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea; (A.C.); (N.J.); (S.A.)
| | - Naeun Jang
- School of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea; (A.C.); (N.J.); (S.A.)
| | - Seonhwa Ahn
- School of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea; (A.C.); (N.J.); (S.A.)
| | - Mohammad Shafiqul Islam
- Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea; (A.G.); (M.S.I.)
| | - Sheikh Mansoor
- Department of Plant Resources and Environment, Jeju National University, Jeju 63243, Republic of Korea;
| | - Yong Suk Chung
- Department of Plant Resources and Environment, Jeju National University, Jeju 63243, Republic of Korea;
| | - Yoonha Kim
- Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Republic of Korea; (A.G.); (M.S.I.)
- Upland Field Machinery Research Center, Kyungpook National University, Daegu 41566, Republic of Korea
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6
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Wang T, Crawford MM, Tuinstra MR. A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers. FRONTIERS IN PLANT SCIENCE 2023; 14:1138479. [PMID: 37113602 PMCID: PMC10126475 DOI: 10.3389/fpls.2023.1138479] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 03/14/2023] [Indexed: 06/19/2023]
Abstract
Yield for biofuel crops is measured in terms of biomass, so measurements throughout the growing season are crucial in breeding programs, yet traditionally time- and labor-consuming since they involve destructive sampling. Modern remote sensing platforms, such as unmanned aerial vehicles (UAVs), can carry multiple sensors and collect numerous phenotypic traits with efficient, non-invasive field surveys. However, modeling the complex relationships between the observed phenotypic traits and biomass remains a challenging task, as the ground reference data are very limited for each genotype in the breeding experiment. In this study, a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) model is proposed for sorghum biomass prediction. The architecture is designed to exploit the time series remote sensing and weather data, as well as static genotypic information. As a large number of features have been derived from the remote sensing data, feature importance analysis is conducted to identify and remove redundant features. A strategy to extract representative information from high-dimensional genetic markers is proposed. To enhance generalization and minimize the need for ground reference data, transfer learning strategies are proposed for selecting the most informative training samples from the target domain. Consequently, a pre-trained model can be refined with limited training samples. Field experiments were conducted over a sorghum breeding trial planted in multiple years with more than 600 testcross hybrids. The results show that the proposed LSTM-based RNN model can achieve high accuracies for single year prediction. Further, with the proposed transfer learning strategies, a pre-trained model can be refined with limited training samples from the target domain and predict biomass with an accuracy comparable to that from a trained-from-scratch model for both multiple experiments within a given year and across multiple years.
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Affiliation(s)
- Taojun Wang
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States
| | - Melba M. Crawford
- Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, United States
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
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7
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Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis. Foods 2023; 12:foods12061242. [PMID: 36981168 PMCID: PMC10048131 DOI: 10.3390/foods12061242] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/06/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023] Open
Abstract
Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.
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8
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Yan J, Wang X. Machine learning bridges omics sciences and plant breeding. TRENDS IN PLANT SCIENCE 2023; 28:199-210. [PMID: 36153276 DOI: 10.1016/j.tplants.2022.08.018] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 08/15/2022] [Accepted: 08/23/2022] [Indexed: 06/16/2023]
Abstract
Some of the biological knowledge obtained from fundamental research will be implemented in applied plant breeding. To bridge basic research and breeding practice, machine learning (ML) holds great promise to translate biological knowledge and omics data into precision-designed plant breeding. Here, we review ML for multi-omics analysis in plants, including data dimensionality reduction, inference of gene-regulation networks, and gene discovery and prioritization. These applications will facilitate understanding trait regulation mechanisms and identifying target genes potentially applicable to knowledge-driven molecular design breeding. We also highlight applications of deep learning in plant phenomics and ML in genomic selection-assisted breeding, such as various ML algorithms that model the correlations among genotypes (genes), phenotypes (traits), and environments, to ultimately achieve data-driven genomic design breeding.
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Affiliation(s)
- Jun Yan
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China; Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing 100094, China
| | - Xiangfeng Wang
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China; Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing 100094, China.
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Study on Rice Grain Mildewed Region Recognition Based on Microscopic Computer Vision and YOLO-v5 Model. Foods 2022; 11:foods11244031. [PMID: 36553773 PMCID: PMC9777938 DOI: 10.3390/foods11244031] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/07/2022] [Accepted: 12/09/2022] [Indexed: 12/15/2022] Open
Abstract
This study aims to develop a high-speed and nondestructive mildewed rice grain detection method. First, a set of microscopic images of rice grains contaminated by Aspergillus niger, Penicillium citrinum, and Aspergillus cinerea are acquired to serve as samples, and the mildewed regions are marked. Then, three YOLO-v5 models for identifying regions of rice grain with contamination of Aspergillus niger, Penicillium citrinum, and Aspergillus cinerea in microscopic images are established. Finally, the relationship between the proportion of mildewed regions and the total number of colonies is analyzed. The results show that the proposed YOLO-v5 models achieve accuracy levels of 89.26%, 91.15%, and 90.19% when detecting mildewed regions with contamination of Aspergillus niger, Penicillium citrinum, and Aspergillus cinerea in the microscopic images of the verification set. The proportion of the mildewed region area of rice grain with contamination of Aspergillus niger/Penicillium citrinum/Aspergillus cinerea is logarithmically correlated with the logarithm of the total number of colonies (TVC). The corresponding determination coefficients are 0.7466, 0.7587, and 0.8148, respectively. This study provides a reference for future research on high-speed mildewed rice grain detection methods based on MCV technology.
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Jin C, Liu S, Chen M. Semantic segmentation-based mechanized harvesting soybean quality
detection. Sci Prog 2022; 105:00368504221108518. [PMCID: PMC10306129 DOI: 10.1177/00368504221108518] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/11/2024]
Abstract
Crushing rate and impurity rate are important quality indicators of mechanically harvested soybeans. Intelligent quality detection of mechanically harvested soybeans based on machine vision is of great significance to evaluate soybean quality accurately and rapidly. This study proposes an improved U-Net method for identifying intact soybean grains, crushing soybean grains, and impurities. Based on the accurate identification of soybean components and using the quantitative model of soybean crushing rate and impurity rate, the quality of soybean samples can be detected in real-time. To this end, a soybean quality inspection system is designed to realize the dynamic collection and detection of soybean samples. The test results show that the comprehensive evaluation index values of the improved U-Net segmentation algorithm in identifying intact soybean grains, crushing soybean grains, and impurities are 93.04%, 89.40%, and 96.49%, respectively. Compared with the traditional U-Net model, the performance of the indicators is improved by 3.23%, 0.17% and 0.72%, respectively. Compared with manual detection, the maximum absolute error of the crushing rate detection of the soybean quality detection system is 0.57%, and the maximum absolute error of the impurity rate detection is 0.69%. The proposed soybean quality inspection system can be used as an effective tool for real-time online inspection of soybean quality.
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Affiliation(s)
- Chengqian Jin
- Nanjing Research Institute for Agricultural
Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing, PR
China
| | - Shikun Liu
- Nanjing Research Institute for Agricultural
Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing, PR
China
| | - Man Chen
- Nanjing Research Institute for Agricultural
Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing, PR
China
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11
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Miao Y, Wang L, Peng C, Li H, Li X, Zhang M. Banana plant counting and morphological parameters measurement based on terrestrial laser scanning. PLANT METHODS 2022; 18:66. [PMID: 35585596 PMCID: PMC9118865 DOI: 10.1186/s13007-022-00894-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 04/25/2022] [Indexed: 05/20/2023]
Abstract
BACKGROUND The number of banana plants is closely related to banana yield. The diameter and height of the pseudo-stem are important morphological parameters of banana plants, which can reflect the growth status and vitality. To address the problems of high labor intensity and subjectivity in traditional measurement methods, a fast measurement method for banana plant count, pseudo-stem diameter, and height based on terrestrial laser scanning (TLS) was proposed. RESULTS First, during the nutritional growth period of banana, three-dimensional (3D) point cloud data of two measured fields were obtained by TLS. Second, the point cloud data was preprocessed. And the single plant segmentation of the canopy closed banana plant point cloud was realized furtherly. Finally, the number of banana plants was obtained by counting the number of pseudo-stems, and the diameter of pseudo-stems was measured using a cylindrical segmentation algorithm. A sliding window recognition method was proposed to determine the junction position between leaves and pseudo-stems, and the height of the pseudo-stems was measured. Compared with the measured value of artificial point cloud, when counting the number of banana plants, the precision,recall and percentage error of field 1 were 93.51%, 94.02%, and 0.54% respectively; the precision,recall and percentage error of field 2 were 96.34%, 92.00%, and 4.5% respectively; In the measurement of pseudo-stem diameter and height of banana, the root mean square error (RMSE) of pseudo-stem diameter and height of banana plant in field 1 were 0.38 cm and 0.2014 m respectively, and the mean absolute percentage error (MAPE) were 1.30% and 5.11% respectively; the RMSE of pseudo-stem diameter and height of banana plant in field 2 were 0.39 cm and 0.2788 m respectively, and the MAPE were 1.04% and 9.40% respectively. CONCLUSION The results show that the method proposed in this paper is suitable for the field measurement of banana count, pseudo-stem diameter, and height and can provide a fast field measurement method for banana plantation management.
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Affiliation(s)
- Yanlong Miao
- Key Lab of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing, 100083, China
| | - Liuyang Wang
- Key Lab of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, 100083, China
| | - Cheng Peng
- Key Lab of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, 100083, China
| | - Han Li
- Key Lab of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, 100083, China
| | - Xiuhua Li
- College of Electrical Engineering, Guangxi University, Nanning, 530004, Guangxi, China
| | - Man Zhang
- Key Lab of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing, 100083, China.
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12
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Cao P, Zhao Y, Wu F, Xin D, Liu C, Wu X, Lv J, Chen Q, Qi Z. Multi-Omics Techniques for Soybean Molecular Breeding. Int J Mol Sci 2022; 23:4994. [PMID: 35563386 PMCID: PMC9099442 DOI: 10.3390/ijms23094994] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 04/22/2022] [Accepted: 04/28/2022] [Indexed: 02/04/2023] Open
Abstract
Soybean is a major crop that provides essential protein and oil for food and feed. Since its origin in China over 5000 years ago, soybean has spread throughout the world, becoming the second most important vegetable oil crop and the primary source of plant protein for global consumption. From early domestication and artificial selection through hybridization and ultimately molecular breeding, the history of soybean breeding parallels major advances in plant science throughout the centuries. Now, rapid progress in plant omics is ushering in a new era of precision design breeding, exemplified by the engineering of elite soybean varieties with specific oil compositions to meet various end-use targets. The assembly of soybean reference genomes, made possible by the development of genome sequencing technology and bioinformatics over the past 20 years, was a great step forward in soybean research. It facilitated advances in soybean transcriptomics, proteomics, metabolomics, and phenomics, all of which paved the way for an integrated approach to molecular breeding in soybean. In this review, we summarize the latest progress in omics research, highlight novel findings made possible by omics techniques, note current drawbacks and areas for further research, and suggest that an efficient multi-omics approach may accelerate soybean breeding in the future. This review will be of interest not only to soybean breeders but also to researchers interested in the use of cutting-edge omics technologies for crop research and improvement.
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Affiliation(s)
- Pan Cao
- College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (P.C.); (Y.Z.); (F.W.); (D.X.); (C.L.)
| | - Ying Zhao
- College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (P.C.); (Y.Z.); (F.W.); (D.X.); (C.L.)
| | - Fengjiao Wu
- College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (P.C.); (Y.Z.); (F.W.); (D.X.); (C.L.)
| | - Dawei Xin
- College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (P.C.); (Y.Z.); (F.W.); (D.X.); (C.L.)
| | - Chunyan Liu
- College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (P.C.); (Y.Z.); (F.W.); (D.X.); (C.L.)
| | - Xiaoxia Wu
- College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (P.C.); (Y.Z.); (F.W.); (D.X.); (C.L.)
| | - Jian Lv
- Department of Innovation, Syngenta Biotechnology China, Beijing 102206, China
| | - Qingshan Chen
- College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (P.C.); (Y.Z.); (F.W.); (D.X.); (C.L.)
| | - Zhaoming Qi
- College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (P.C.); (Y.Z.); (F.W.); (D.X.); (C.L.)
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High-Throughput Legume Seed Phenotyping Using a Handheld 3D Laser Scanner. REMOTE SENSING 2022. [DOI: 10.3390/rs14020431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
High-throughput phenotyping involves many samples and diverse trait types. For the goal of automatic measurement and batch data processing, a novel method for high-throughput legume seed phenotyping is proposed. A pipeline of automatic data acquisition and processing, including point cloud acquisition, single-seed extraction, pose normalization, three-dimensional (3D) reconstruction, and trait estimation, is proposed. First, a handheld laser scanner is used to obtain the legume seed point clouds in batches. Second, a combined segmentation method using the RANSAC method, the Euclidean segmentation method, and the dimensionality of the features is proposed to conduct single-seed extraction. Third, a coordinate rotation method based on PCA and the table normal is proposed to conduct pose normalization. Fourth, a fast symmetry-based 3D reconstruction method is built to reconstruct a 3D model of the single seed, and the Poisson surface reconstruction method is used for surface reconstruction. Finally, 34 traits, including 11 morphological traits, 11 scale factors, and 12 shape factors, are automatically calculated. A total of 2500 samples of five kinds of legume seeds are measured. Experimental results show that the average accuracies of scanning and segmentation are 99.52% and 100%, respectively. The overall average reconstruction error is 0.014 mm. The average morphological trait measurement accuracy is submillimeter, and the average relative percentage error is within 3%. The proposed method provides a feasible method of batch data acquisition and processing, which will facilitate the automation in high-throughput legume seed phenotyping.
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14
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A Deep-Learning-Based Approach for Wheat Yellow Rust Disease Recognition from Unmanned Aerial Vehicle Images. SENSORS 2021; 21:s21196540. [PMID: 34640873 PMCID: PMC8513082 DOI: 10.3390/s21196540] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 09/24/2021] [Accepted: 09/26/2021] [Indexed: 11/17/2022]
Abstract
Yellow rust is a disease with a wide range that causes great damage to wheat. The traditional method of manually identifying wheat yellow rust is very inefficient. To improve this situation, this study proposed a deep-learning-based method for identifying wheat yellow rust from unmanned aerial vehicle (UAV) images. The method was based on the pyramid scene parsing network (PSPNet) semantic segmentation model to classify healthy wheat, yellow rust wheat, and bare soil in small-scale UAV images, and to investigate the spatial generalization of the model. In addition, it was proposed to use the high-accuracy classification results of traditional algorithms as weak samples for wheat yellow rust identification. The recognition accuracy of the PSPNet model in this study reached 98%. On this basis, this study used the trained semantic segmentation model to recognize another wheat field. The results showed that the method had certain generalization ability, and its accuracy reached 98%. In addition, the high-accuracy classification result of a support vector machine was used as a weak label by weak supervision, which better solved the labeling problem of large-size images, and the final recognition accuracy reached 94%. Therefore, the present study method facilitated timely control measures to reduce economic losses.
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Kienbaum L, Correa Abondano M, Blas R, Schmid K. DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics. PLANT METHODS 2021; 17:91. [PMID: 34419093 PMCID: PMC8379755 DOI: 10.1186/s13007-021-00787-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 07/30/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Maize cobs are an important component of crop yield that exhibit a high diversity in size, shape and color in native landraces and modern varieties. Various phenotyping approaches were developed to measure maize cob parameters in a high throughput fashion. More recently, deep learning methods like convolutional neural networks (CNNs) became available and were shown to be highly useful for high-throughput plant phenotyping. We aimed at comparing classical image segmentation with deep learning methods for maize cob image segmentation and phenotyping using a large image dataset of native maize landrace diversity from Peru. RESULTS Comparison of three image analysis methods showed that a Mask R-CNN trained on a diverse set of maize cob images was highly superior to classical image analysis using the Felzenszwalb-Huttenlocher algorithm and a Window-based CNN due to its robustness to image quality and object segmentation accuracy ([Formula: see text]). We integrated Mask R-CNN into a high-throughput pipeline to segment both maize cobs and rulers in images and perform an automated quantitative analysis of eight phenotypic traits, including diameter, length, ellipticity, asymmetry, aspect ratio and average values of red, green and blue color channels for cob color. Statistical analysis identified key training parameters for efficient iterative model updating. We also show that a small number of 10-20 images is sufficient to update the initial Mask R-CNN model to process new types of cob images. To demonstrate an application of the pipeline we analyzed phenotypic variation in 19,867 maize cobs extracted from 3449 images of 2484 accessions from the maize genebank of Peru to identify phenotypically homogeneous and heterogeneous genebank accessions using multivariate clustering. CONCLUSIONS Single Mask R-CNN model and associated analysis pipeline are widely applicable tools for maize cob phenotyping in contexts like genebank phenomics or plant breeding.
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Affiliation(s)
- Lydia Kienbaum
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart, Germany
| | - Miguel Correa Abondano
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart, Germany
| | - Raul Blas
- Universidad National Agraria La Molina (UNALM), Lima, Peru
| | - Karl Schmid
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart, Germany.
- Computational Science Lab, University of Hohenheim, Stuttgart, Germany.
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16
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Dong Z, Liu Y, Ci B, Wen M, Li M, Lu X, Feng X, Wen S, Ma F. Estimation of nitrate nitrogen content in cotton petioles under drip irrigation based on wavelet neural network approach using spectral indices. PLANT METHODS 2021; 17:90. [PMID: 34407848 PMCID: PMC8371893 DOI: 10.1186/s13007-021-00790-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 08/02/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Estimation of nitrate nitrogen (NO3--N) content in petioles is one of the key approaches for monitoring nitrogen (N) nutrition in crops. Rapid, non-destructive, and accurate evaluation of NO3--N contents in cotton petioles under drip irrigation is of great significance. METHODS In this study, we discussed the use of hyperspectral data to estimate NO3--N contents in cotton petioles under drip irrigation at different N treatments and growth stages. The correlations among trilateral parameters and six vegetation indices and petiole NO3--N contents were first investigated, after which a traditional regression model for petioles NO3--N content was established. A wavelet neural network (WNN) model for estimating petiole NO3--N content was also established. In addition, the performance of WNN was compared to those of random forest (RF), radial basis function neural network (RBF) and back propagation neural network (BP). RESULTS Between the blue edge amplitude (Db) and blue edge area (SDb) of the blue edge parameters was the optimal index for the estimation model of petiole NO3--N content. We found that the prediction results of the blue edge parameters and WNN were 7.3% higher than the coefficient of determination (R2) of the first derivative vegetation index and WNN. Root mean square error (RMSE) and mean absolute error (MAE) were 25.2% and 30.9% lower than first derivative vegetation, respectively, and the performance was better than that of RF, RBF and BP. CONCLUSIONS An inexpensive approach consisting of the WNN algorithm and blue edge parameters can be used to enhance the accuracy of NO3--N content estimation in cotton petioles under drip irrigation.
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Affiliation(s)
- Zhiqiang Dong
- School of Agriculture, Shihezi University, Shihezi, 832003, Xinjiang, People's Republic of China
- National and Local Joint Engineering Research Center of Information Management and Application Technology for Modern Agricultural Production (XPCC), Shihezi, 832003, People's Republic of China
| | - Yang Liu
- School of Agriculture, Shihezi University, Shihezi, 832003, Xinjiang, People's Republic of China.
- National and Local Joint Engineering Research Center of Information Management and Application Technology for Modern Agricultural Production (XPCC), Shihezi, 832003, People's Republic of China.
| | - Baoxia Ci
- School of Agriculture, Shihezi University, Shihezi, 832003, Xinjiang, People's Republic of China
- National and Local Joint Engineering Research Center of Information Management and Application Technology for Modern Agricultural Production (XPCC), Shihezi, 832003, People's Republic of China
| | - Ming Wen
- School of Agriculture, Shihezi University, Shihezi, 832003, Xinjiang, People's Republic of China
- National and Local Joint Engineering Research Center of Information Management and Application Technology for Modern Agricultural Production (XPCC), Shihezi, 832003, People's Republic of China
| | - Minghua Li
- School of Agriculture, Shihezi University, Shihezi, 832003, Xinjiang, People's Republic of China
- National and Local Joint Engineering Research Center of Information Management and Application Technology for Modern Agricultural Production (XPCC), Shihezi, 832003, People's Republic of China
| | - Xi Lu
- School of Agriculture, Shihezi University, Shihezi, 832003, Xinjiang, People's Republic of China
- National and Local Joint Engineering Research Center of Information Management and Application Technology for Modern Agricultural Production (XPCC), Shihezi, 832003, People's Republic of China
| | - Xiaokang Feng
- School of Agriculture, Shihezi University, Shihezi, 832003, Xinjiang, People's Republic of China
- National and Local Joint Engineering Research Center of Information Management and Application Technology for Modern Agricultural Production (XPCC), Shihezi, 832003, People's Republic of China
| | - Shuai Wen
- School of Agriculture, Shihezi University, Shihezi, 832003, Xinjiang, People's Republic of China
- National and Local Joint Engineering Research Center of Information Management and Application Technology for Modern Agricultural Production (XPCC), Shihezi, 832003, People's Republic of China
| | - Fuyu Ma
- School of Agriculture, Shihezi University, Shihezi, 832003, Xinjiang, People's Republic of China.
- National and Local Joint Engineering Research Center of Information Management and Application Technology for Modern Agricultural Production (XPCC), Shihezi, 832003, People's Republic of China.
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