Real-time recognition system of soybean seed full-surface defects based on deep learning

Zhao, Guoyang; Quan, Longzhe; Li, Hailong; Feng, Huaiqu; Li, Songwei; Zhang, Shuhan; Liu, Ruiqi

doi:10.1016/j.compag.2021.106230

Cited by 61 publications

(27 citation statements)

References 32 publications

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“…Table 4 has listed some of the future research scopes that could be pursued in the context of smart indoor farms. [188]. Improvement of accuracy in the present Computer Vision plant phenotyping methods using Deep Learning [189][190][191][192], Fuzzy [193] logic, etc.…”

Section: Discussion and Future Research Scopementioning

confidence: 99%

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Hati

Singh

2021

AgriEngineering

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Conventional farming necessitates a large number of resources and infrastructure such as land, irrigation, manpower to manage farms, etc. Modern initiatives are required to automate conventional farms. Smart indoor farms offer the potential to remedy the shortfalls of conventional farms by providing a controlled, intelligent, and smart environment. This paper presents a three-dimensional perspective consisting of soilless farming, energy harvesting, and smart technologies, which could be considered as the three important characteristics of smart indoor farms. A six-layer smart indoor farms architecture has also been proposed, which explains how data are collected using various sensors and devices and then transmitted onto the cloud infrastructure for further analysis and control through various layers. Artificial lighting, smart nutrition management, and artificial climate control, to name a few, are some of the important requirements for smart indoor farms while considering control and service management factors. The major bottleneck in installing such systems is both the economical and the technical constraints. However, with the evolution of technology (and when they become widely available in the near future), a more favourable farming scenario may emerge. Furthermore, smart indoor farms could be viewed as a potential answer for meeting the demands of a sustainable agricultural revolution as we move closer to Agriculture 4.0. Finally, in order to adapt smart indoor farms and their study scope, our work has presented various research areas to potential researchers.

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Section: Discussion and Future Research Scopementioning

confidence: 99%

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Hati

Singh

2021

AgriEngineering

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“…Jitanan [7] et al constructed SVM and used the color histogram of H components in the HSI model and GLCM statistics to represent the color and texture features to realize soybean seeds class. Zhao [8] et al proposed the whole surface classification system of soybean seeds, which solved the problem that the soybean classification system only focused on the recognition of one surface of soybean, and realized the classification of the whole surface of soybean seeds. The above literature has achieved highprecision classification and identification of agricultural products, but they all studied the classification of crop seeds under ideal conditions and did not consider the overlapping and adhesion problems in the actual detection process.…”

Section: Mvaid-2022mentioning

confidence: 99%

Soybean image segmentation based on multi-scale Retinex with color restoration

Lin¹,

Ye²

2022

J. Phys.: Conf. Ser.

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Aiming at the matter of seed overlapping and adhesion within the actual quality detection of soybean, a soybean image segmentation based on multi-scale Retinex with color restoration (MSRCR) was proposed. Firstly, original images of soybean were obtained by the industrial camera. secondly, these images were enhanced by MSRCR. Thirdly, these images after MSRCR were segmented by Otsu. Finally, soybean seed images were extracted by mask and minimum bounding rectangle from soybean images after MSRCR. The image acquisition system was constructed and the algorithm was verified. The experiment showed that the segmentation accuracy of the proposed algorithm was 98.05%, which can meet the requirement of soybean image segmentation. Additionally, the proposed algorithm can also provide an image preprocessing method for agricultural seed classification in similar scenes.

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“…Deep learning techniques have developed rapidly. The convolutional neural network (CNN) is a part of them, which has strong self-learning ability, adaptability, and generalization [2,10,11]. It has achieved considerable success in image classification, object detection, and face recognition [12].…”

Section: Introductionmentioning

confidence: 99%

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

Tan

Zhang

et al. 2022

Agriculture

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Maize is one of the essential crops for food supply. Accurate sorting of seeds is critical for cultivation and marketing purposes, while the traditional methods of variety identification are time-consuming, inefficient, and easily damaged. This study proposes a rapid classification method for maize seeds using a combination of machine vision and deep learning. 8080 maize seeds of five varieties were collected, and then the sample images were classified into training and validation sets in the proportion of 8:2, and the data were enhanced. The proposed improved network architecture, namely P-ResNet, was fine-tuned for transfer learning to recognize and categorize maize seeds, and then it compares the performance of the models. The results show that the overall classification accuracy was determined as 97.91, 96.44, 99.70, 97.84, 98.58, 97.13, 96.59, and 98.28% for AlexNet, VGGNet, P-ResNet, GoogLeNet, MobileNet, DenseNet, ShuffleNet, and EfficientNet, respectively. The highest classification accuracy result was obtained with P-ResNet, and the model loss remained at around 0.01. This model obtained the accuracy of classifications for BaoQiu, ShanCu, XinNuo, LiaoGe, and KouXian varieties, which reached 99.74, 99.68, 99.68, 99.61, and 99.80%, respectively. The experimental results demonstrated that the convolutional neural network model proposed enables the effective classification of maize seeds. It can provide a reference for identifying seeds of other crops and be applied to consumer use and the food industry.

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Real-time recognition system of soybean seed full-surface defects based on deep learning

Cited by 61 publications

References 32 publications

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Soybean image segmentation based on multi-scale Retinex with color restoration

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

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