Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning

Xu, Peng; Tan, Qian; Zhang, Yunpeng; Zha, Xiantao; Yang, Shiping; Yang, Ranbing

doi:10.3390/agriculture12020232

Cited by 46 publications

(28 citation statements)

References 31 publications

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“…Similar studies can be found in [23][24][25][26][27][28] where classifiers were trained after feature extraction which were mostly based on color, shape, and/or morphology. In the same era, deep learning architectures have also been proposed for grain classification [21,26,[29][30][31][32][33][34]. The employed deep learning approaches used in these studies were quite deep and large.…”

Section: Literature Reviewmentioning

confidence: 99%

On Application of Lightweight Models for Rice Variety Classification and Their Potential in Edge Computing

Iqbal,

Aasem,

Ahmad

et al. 2023

Foods

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Rice is one of the fundamental food items that comes in many varieties with their associated benefits. It can be sub-categorized based on its visual features like texture, color, and shape. Using these features, the automatic classification of rice varieties has been studied using various machine learning approaches for marketing and industrial use. Due to the outstanding performance of deep learning, several models have been proposed to assist in vision tasks like classification and detection. Regardless of their best results on accuracy metrics, they have been observed as overly excessive for computational resources and expert supervision. To address these challenges, this paper proposes three deep learning models that offer similar performance with 10% lighter computational overhead in comparison to existing best models. Moreover, they have been trained for end-to-end flow to demonstrate minimum expert supervision for pre-processing and feature engineering sub-tasks. The results can be observed as promising for classifying rice among five varieties, namely Arborio, Basmati, Ipsala, Jasmine, and Karacadag. The process and performance of the trained models can be extended for edge and mobile devices for field-specific tasks autonomously.

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Section: Literature Reviewmentioning

confidence: 99%

On Application of Lightweight Models for Rice Variety Classification and Their Potential in Edge Computing

Iqbal,

Aasem,

Ahmad

et al. 2023

Foods

View full text Add to dashboard Cite

show abstract

“…Meanwhile, some other typical models were then developed in the study, which showed that the CK-CNN achieved the best robustness and stability compared with VGG16 and ResNet50. It is worth mentioning that the newly improved model architectures combined with transfer learning such as P-ResNet showed the best accuracy to classify maize seeds in a non-destructive, fast and efficient manner [118]. The process is given in Figure 6.…”

Section: Crop Seed Variety Classificationmentioning

confidence: 99%

“…The result highlighted the advantages of transfer learning and its potential in deep learning, providing new solutions for CNN-based computer vision and spectroscopic techniques for seed classification and detection. It is worth mentioning that the newly improved model architectures combined with transfer learning such as P-ResNet showed the best accuracy to classify maize seeds in a non-destructive, fast and efficient manner [118]. The process is given in Figure 6.…”

Section: Crop Seed Variety Classificationmentioning

confidence: 99%

Convolutional Neural Networks in Computer Vision for Grain Crop Phenotyping: A Review

Wang

2022

Agronomy

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Computer vision (CV) combined with a deep convolutional neural network (CNN) has emerged as a reliable analytical method to effectively characterize and quantify high-throughput phenotyping of different grain crops, including rice, wheat, corn, and soybean. In addition to the ability to rapidly obtain information on plant organs and abiotic stresses, and the ability to segment crops from weeds, such techniques have been used to detect pests and plant diseases and to identify grain varieties. The development of corresponding imaging systems to assess the phenotypic parameters, yield, and quality of crop plants will increase the confidence of stakeholders in grain crop cultivation, thereby bringing technical and economic benefits to advanced agriculture. Therefore, this paper provides a comprehensive review of CNNs in computer vision for grain crop phenotyping. It is meaningful to provide a review as a roadmap for future research in such a thriving research area. The CNN models (e.g., VGG, YOLO, and Faster R-CNN) used CV tasks including image classification, object detection, semantic segmentation, and instance segmentation, and the main results of recent studies on crop phenotype detection are discussed and summarized. Additionally, the challenges and future trends of the phenotyping techniques in grain crops are presented.

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“…This method exhibited substantial enhancements in accuracy and efficiency compared to traditional color selection equipment. In a similar vein, Xu et al (2022) used a CNN model to automatically classify five types of corn seeds, achieving notably superior classification outcomes compared to manual selection. They introduced a rapid corn seed classification method that amalgamates machine vision and deep learning.…”

Section: Introductionmentioning

confidence: 99%

Research on lightweight detection algorithm for millet quality grading

Wang,

Shao,

Tian

et al. 2024

J Food Process Engineering

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Accurately assessing the quality of millet is of paramount importance for its storage, processing, and consumption. In complex environments, rapidly and precisely identifying densely packed millet poses a significant challenge in the quality grading process. This paper introduces an improved version of the YOLOv7 lightweight detection algorithm. The key enhancement involves integrating the super lightweight target detection algorithm ESNet. ESNet optimizes the lightweight design of the neck by enhancing the backbone network, thereby strengthening the network's feature extraction capabilities. Furthermore, by combining Soft non‐maximum suppression (NMS) with the EIOU Loss function, the accuracy of detecting dense millet in occluded scenarios is further optimized. Various models were evaluated using the millet dataset in this study. During the testing phase, images containing densely packed millet were randomly selected to assess the effectiveness of the models. The test results demonstrate that, compared to lightweight network architectures such as LCNet, ShuffleNetV2, and MobileNetV3, the improved YOLOv7‐ESNet model achieves higher average accuracy, precision, recall, and comprehensive evaluation metrics in detection. Additionally, by combining the YOLOv7‐ESNet model with Soft‐EIoU NMS, an average accuracy of 93.19% and an average prediction time of 52.5 ms were achieved. These results meet the real‐time grading requirements for millet quality, establishing a robust technical foundation for millet quality grading. This paper overcomes the challenge of precise millet detection in complex scenarios, providing a valuable contribution to the field.

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Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning

Cited by 46 publications

References 31 publications

On Application of Lightweight Models for Rice Variety Classification and Their Potential in Edge Computing

On Application of Lightweight Models for Rice Variety Classification and Their Potential in Edge Computing

Convolutional Neural Networks in Computer Vision for Grain Crop Phenotyping: A Review

Research on lightweight detection algorithm for millet quality grading

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