Prediction of Maize Seed Vigor Based on First-Order Difference Characteristics of Hyperspectral Data

Cui, Huawei; Yang, Bing; Zhang, Xiaodi; Wang, Zilin; Li, Longwei; Miao, Aimin

doi:10.3390/agronomy12081899

Cited by 5 publications

(5 citation statements)

References 25 publications

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“…The AT and AFD are the best spectral transformation methods for estimating soil Cd and As contents from HJ-1A HSI spectra, respectively. This is mainly because the AT transformation reduces the influence of multiplicative factors caused by changes in lighting conditions, and the FD transformation can effectively extract and amplify the information implied in the spectra [50,51].…”

Section: Effect Of Spectral Pre-processing and Ga-plsr On Modeling Pe...mentioning

confidence: 99%

Multi-Scale Stereoscopic Hyperspectral Remote Sensing Estimation of Heavy Metal Contamination in Wheat Soil over a Large Area of Farmland

Zhong,

Chu,

Qian

et al. 2023

Agronomy

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With the rapid development of China’s industrialization and urbanization, the problem of heavy metal pollution in soil has become increasingly prominent, seriously threatening the safety of the ecosystem and human health. The development of hyperspectral remote sensing technology provides the possibility to achieve the rapid and non-destructive monitoring of soil heavy metal contents. This study aimed to fully explore the potential of ground and satellite image spectra in estimating soil heavy metal contents. We chose Xushe Town, Yixing City, Jiangsu Province as the research area, collected soil samples from farmland over two different periods, and measured the contents of the heavy metals Cd and As in the laboratory. At the same time, under field conditions, we also measured the spectra of wheat leaves and obtained HuanJing-1A HyperSpectral Imager (HJ-1A HSI) satellite image data. We first performed various spectral transformation pre-processing techniques on the leaf and image spectral data. Then, we used genetic algorithm (GA) optimized partial least squares regression (PLSR) to establish an estimation model of the soil heavy metal Cd and As contents, while evaluating the accuracy of the model. Finally, we obtained the best ground and satellite remote sensing estimation models and drew spatial distribution maps of the soil Cd and As contents in the study area. The results showed the following: (1) spectral pre-processing techniques can highlight some hidden information in the spectra, including mathematical transformations such as differentiation; (2) in ground and satellite spectral modeling, the GA-PLSR model has higher accuracy than PLSR, and using a GA for spectral band selection can improve the model’s accuracy and stability; (3) wheat leaf spectra provide a good ability to estimate soil Cd (relative percent difference (RPD) = 2.72) and excellent ability to estimate soil As (RPD = 3.25); HJ-1A HSI image spectra only provide the possibility of distinguishing high and low values of soil Cd and As (RPD = 1.87, RPD = 1.91). Therefore, it is possible to indirectly estimate soil heavy metal Cd and As contents using wheat leaf hyperspectral data, and HJ-1A HSI image spectra can also identify areas of key pollution.

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Section: Effect Of Spectral Pre-processing and Ga-plsr On Modeling Pe...mentioning

confidence: 99%

Multi-Scale Stereoscopic Hyperspectral Remote Sensing Estimation of Heavy Metal Contamination in Wheat Soil over a Large Area of Farmland

Zhong,

Chu,

Qian

et al. 2023

Agronomy

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“…Firstly, we utilize a deep learning-based model, YOLOv8, for automated seed segmentation and classification. As shown in Table 4, most prior works rely on traditional machine learning techniques [3,13,25,27] without leveraging the representation learning capabilities of deep neural networks. By using the Darknet53 CNN backbone, our method can extract robust spatial features predictive of seed vigor levels.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, our solution provides a low-cost alternative suitable for batch analysis of seed lots, addressing limitations in techniques requiring expensive hyperspectral cameras [3,13,16,25,28] or destructive biochemical testing [27]. The use of widely accessible X-ray RGB imagery, correctly segmented over 95% of the time by YOLOv8, offers an affordable option for seed producers compared to hyperspectral imaging utilized in several related papers.…”

Section: Discussionmentioning

confidence: 99%

“…Recent research in the literature has explored various computational methods that correlate image-derived parameters with seed quality standards [5,10,11]. Image analysis techniques provide non-destructive means to comprehend various aspects of seed development, establishing a connection between internal morphology and structural integrity [8,[12][13][14]. This approach enables the determination of the physiological potential of seed lots.…”

Section: Introductionmentioning

confidence: 99%

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Applying YOLOv8 and X-ray Morphology Analysis to Assess the Vigor of Brachiaria brizantha cv. Xaraés Seeds

da Silva,

de Freitas,

Abud

et al. 2024

AgriEngineering

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Seed quality significantly affects how well crops grow. Traditional methods for checking seed quality, like seeing how many seeds sprout or using a chemical test called tetrazolium testing, require people to look at the seeds closely, which takes a lot of time and effort. Nowadays, computer vision, a technology that helps computers see and understand images, is being used more in farming. Here, we use computer vision with X-ray imaging to assist experts in rapidly and accurately assessing seed quality. We looked at three different sets of seeds using X-ray images and used YOLOv8 to analyze them. YOLOv8 software measures different aspects about seeds, like their size and the area taken up by the part inside, called the endosperm. Based on this information, we put the seeds into four groups depending on how much endosperm they have. Our results show that the YOLOv8 program works well in identifying and separating the endosperm, even with a small amount of data. Our method was able to accurately identify the endosperm about 95.6% of the time. This means that our approach can help determine how effective the seeds are to plant crops.

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“…Masood et al (2021) propose an automated method that utilizes the Mask RCNN model to achieve precise localization and segmentation of brain tumors. Cui et al (2022) constructed a recognition model using hyperspectral data and feature extraction algorithms to predict maize root length, showing a significant correlation between root length and viability. Therefore, it is of great significance to measure and predict the seed viability using computer technology.…”

Section: Introductionmentioning

confidence: 99%

Non-destructive detection of single-seed viability in maize using hyperspectral imaging technology and multi-scale 3D convolutional neural network

Fan,

An,

Wang

et al. 2023

Front. Plant Sci.

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The viability of Zea mays seed plays a critical role in determining the yield of corn. Therefore, developing a fast and non-destructive method is essential for rapid and large-scale seed viability detection and is of great significance for agriculture, breeding, and germplasm preservation. In this study, hyperspectral imaging (HSI) technology was used to obtain images and spectral information of maize seeds with different aging stages. To reduce data input and improve model detection speed while obtaining more stable prediction results, successive projections algorithm (SPA) was used to extract key wavelengths that characterize seed viability, then key wavelength images of maize seed were divided into small blocks with 5 pixels ×5 pixels and fed into a multi-scale 3D convolutional neural network (3DCNN) for further optimizing the discrimination possibility of single-seed viability. The final discriminant result of single-seed viability was determined by comprehensively evaluating the result of all small blocks belonging to the same seed with the voting algorithm. The results showed that the multi-scale 3DCNN model achieved an accuracy of 90.67% for the discrimination of single-seed viability on the test set. Furthermore, an effort to reduce labor and avoid the misclassification caused by human subjective factors, a YOLOv7 model and a Mask R-CNN model were constructed respectively for germination judgment and bud length detection in this study, the result showed that mean average precision (mAP) of YOLOv7 model could reach 99.7%, and the determination coefficient of Mask R-CNN model was 0.98. Overall, this study provided a feasible solution for detecting maize seed viability using HSI technology and multi-scale 3DCNN, which was crucial for large-scale screening of viable seeds. This study provided theoretical support for improving planting quality and crop yield.

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Prediction of Maize Seed Vigor Based on First-Order Difference Characteristics of Hyperspectral Data

Cited by 5 publications

References 25 publications

Multi-Scale Stereoscopic Hyperspectral Remote Sensing Estimation of Heavy Metal Contamination in Wheat Soil over a Large Area of Farmland

Multi-Scale Stereoscopic Hyperspectral Remote Sensing Estimation of Heavy Metal Contamination in Wheat Soil over a Large Area of Farmland

Applying YOLOv8 and X-ray Morphology Analysis to Assess the Vigor of Brachiaria brizantha cv. Xaraés Seeds

Non-destructive detection of single-seed viability in maize using hyperspectral imaging technology and multi-scale 3D convolutional neural network

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