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Li J, Xu F, Song S, Qi J. A maize seed variety identification method based on improving deep residual convolutional network. FRONTIERS IN PLANT SCIENCE 2024; 15:1382715. [PMID: 38803603 PMCID: PMC11128617 DOI: 10.3389/fpls.2024.1382715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 04/19/2024] [Indexed: 05/29/2024]
Abstract
Seed quality and safety are related to national food security, and seed variety purity is an essential indicator in seed quality detection. This study established a maize seed dataset comprising 5877 images of six different types and proposed a maize seed recognition model based on an improved ResNet50 framework. Firstly, we introduced the ResStage structure in the early stage of the original model, which facilitated the network's learning process and enabled more efficient information propagation across the network layers. Meanwhile, in the later residual blocks of the model, we introduced both the efficient channel attention (ECA) mechanism and depthwise separable (DS) convolution, which reduced the model's parameter cost and enabled the capturing of more precise and detailed features. Finally, a Swish-PReLU mixed activation function was introduced globally to improve the overall predictive power of the model. The results showed that our model achieved an impressive accuracy of 91.23% in corn seed classification, surpassing other related models. Compared with the original model, our model improved the accuracy by 7.07%, reduced the loss value by 0.19, and decreased the number of parameters by 40%. The research suggested that this method can efficiently classify corn seeds, holding significant value in seed variety identification.
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Affiliation(s)
- Jian Li
- College of Information Technology, Jilin Agricultural University, Changchun, China
- College of Information Technology, Jilin Bioinformatics Research Center, Changchun, China
| | - Fan Xu
- College of Information Technology, Jilin Agricultural University, Changchun, China
- College of Information Technology, Jilin Bioinformatics Research Center, Changchun, China
| | - Shaozhong Song
- School of Data Science and Artificial Intelligence, Jilin Engineering Normal University, Changchun, China
| | - Ji Qi
- College of Engineering Technical, Jilin Agricultural University, Changchun, China
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Rybacki P, Niemann J, Derouiche S, Chetehouna S, Boulaares I, Seghir NM, Diatta J, Osuch A. Convolutional Neural Network (CNN) Model for the Classification of Varieties of Date Palm Fruits ( Phoenix dactylifera L.). SENSORS (BASEL, SWITZERLAND) 2024; 24:558. [PMID: 38257650 PMCID: PMC10818393 DOI: 10.3390/s24020558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/29/2023] [Accepted: 01/10/2024] [Indexed: 01/24/2024]
Abstract
The popularity and demand for high-quality date palm fruits (Phoenix dactylifera L.) have been growing, and their quality largely depends on the type of handling, storage, and processing methods. The current methods of geometric evaluation and classification of date palm fruits are characterised by high labour intensity and are usually performed mechanically, which may cause additional damage and reduce the quality and value of the product. Therefore, non-contact methods are being sought based on image analysis, with digital solutions controlling the evaluation and classification processes. The main objective of this paper is to develop an automatic classification model for varieties of date palm fruits using a convolutional neural network (CNN) based on two fundamental criteria, i.e., colour difference and evaluation of geometric parameters of dates. A CNN with a fixed architecture was built, marked as DateNET, consisting of a system of five alternating Conv2D, MaxPooling2D, and Dropout classes. The validation accuracy of the model presented in this study depended on the selection of classification criteria. It was 85.24% for fruit colour-based classification and 87.62% for the geometric parameters only; however, it increased considerably to 93.41% when both the colour and geometry of dates were considered.
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Affiliation(s)
- Piotr Rybacki
- Department of Agronomy, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland
| | - Janetta Niemann
- Department of Genetics and Plant Breeding, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland;
| | - Samir Derouiche
- Department of Cellular and Molecular Biology, Faculty of Natural Sciences and Life, University of El Oued, El Oued 39000, Algeria; (S.D.); (I.B.)
- Laboratory of Biodiversity and Application of Biotechnology in the Agricultural Field, Faculty of Natural Sciences and Life, University of El Oued, El Oued 39000, Algeria;
| | - Sara Chetehouna
- Department of Microbiology and Biochemistry, Faculty of Sciences, Mohamed Boudiaf-M’sila University, M’sila 28000, Algeria;
| | - Islam Boulaares
- Department of Cellular and Molecular Biology, Faculty of Natural Sciences and Life, University of El Oued, El Oued 39000, Algeria; (S.D.); (I.B.)
- Laboratory of Biodiversity and Application of Biotechnology in the Agricultural Field, Faculty of Natural Sciences and Life, University of El Oued, El Oued 39000, Algeria;
| | - Nili Mohammed Seghir
- Laboratory of Biodiversity and Application of Biotechnology in the Agricultural Field, Faculty of Natural Sciences and Life, University of El Oued, El Oued 39000, Algeria;
- Department of Agricultural Sciences, University of El Oued, El Oued 39000, Algeria
| | - Jean Diatta
- Department of Agricultural Chemistry and Environmental Biogeochemistry, Poznań University of Life Sciences, Ul. Wojska Polskiego 71F, 60-625 Poznań, Poland;
| | - Andrzej Osuch
- Department of Biosystems Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-637 Poznań, Poland;
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Wang N, Liu H, Li Y, Zhou W, Ding M. Segmentation and Phenotype Calculation of Rapeseed Pods Based on YOLO v8 and Mask R-Convolution Neural Networks. PLANTS (BASEL, SWITZERLAND) 2023; 12:3328. [PMID: 37765490 PMCID: PMC10537308 DOI: 10.3390/plants12183328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 09/13/2023] [Accepted: 09/16/2023] [Indexed: 09/29/2023]
Abstract
Rapeseed is a significant oil crop, and the size and length of its pods affect its productivity. However, manually counting the number of rapeseed pods and measuring the length, width, and area of the pod takes time and effort, especially when there are hundreds of rapeseed resources to be assessed. This work created two state-of-the-art deep learning-based methods to identify rapeseed pods and related pod attributes, which are then implemented in rapeseed pots to improve the accuracy of the rapeseed yield estimate. One of these methods is YOLO v8, and the other is the two-stage model Mask R-CNN based on the framework Detectron2. The YOLO v8n model and the Mask R-CNN model with a Resnet101 backbone in Detectron2 both achieve precision rates exceeding 90%. The recognition results demonstrated that both models perform well when graphic images of rapeseed pods are segmented. In light of this, we developed a coin-based approach for estimating the size of rapeseed pods and tested it on a test dataset made up of nine different species of Brassica napus and one of Brassica campestris L. The correlation coefficients between manual measurement and machine vision measurement of length and width were calculated using statistical methods. The length regression coefficient of both methods was 0.991, and the width regression coefficient was 0.989. In conclusion, for the first time, we utilized deep learning techniques to identify the characteristics of rapeseed pods while concurrently establishing a dataset for rapeseed pods. Our suggested approaches were successful in segmenting and counting rapeseed pods precisely. Our approach offers breeders an effective strategy for digitally analyzing phenotypes and automating the identification and screening process, not only in rapeseed germplasm resources but also in leguminous plants, like soybeans that possess pods.
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Affiliation(s)
- Nan Wang
- The Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, College of Advanced Agricultural Sciences, Zhejiang A&F University, Linan, Hangzhou 311300, China
| | - Hongbo Liu
- The Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, College of Advanced Agricultural Sciences, Zhejiang A&F University, Linan, Hangzhou 311300, China
| | - Yicheng Li
- The Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, College of Advanced Agricultural Sciences, Zhejiang A&F University, Linan, Hangzhou 311300, China
| | - Weijun Zhou
- Institute of Crop Science and Zhejiang Key Laboratory of Crop Germplasm, Zhejiang University, Hangzhou 310058, China
| | - Mingquan Ding
- The Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, College of Advanced Agricultural Sciences, Zhejiang A&F University, Linan, Hangzhou 311300, China
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