Assessment of Performances of Yield and Factors Affecting the Yield in Some Soybean Varieties/Lines Grown Under Semi-Arid Climate Conditions

Arslan, Hüseyin; Karakuş, Mehmet; Hatipoğlu, Halil; Arslan, Doğan; Bayraktar, Önder Volkan

doi:10.15666/aeer/1604_42894298

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…Soybean is one of the most important legume crops used as human food and animal feed in the world; it has 18%–22% oil and 38%–56% vegetable protein in its seeds ( Arslan et al., 2018 ). Soybean has a protective effect against many diseases, such as high cholesterol, osteoporosis, cardiovascular, chronic diseases, and cancers ( Xiao, 2008 ).…”

Section: Introductionmentioning

confidence: 99%

Fusion of hyperspectral imaging (HSI) and RGB for identification of soybean kernel damages using ShuffleNet with convolutional optimization and cross stage partial architecture

Zheng

Zhao²,

Zhu³

et al. 2023

Front. Plant Sci.

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Identification of soybean kernel damages is significant to prevent further disoperation. Hyperspectral imaging (HSI) has shown great potential in cereal kernel identification, but its low spatial resolution leads to external feature infidelity and limits the analysis accuracy. In this study, the fusion of HSI and RGB images and improved ShuffleNet were combined to develop an identification method for soybean kernel damages. First, the HSI-RGB fusion network (HRFN) was designed based on super-resolution and spectral modification modules to process the registered HSI and RGB image pairs and generate super-resolution HSI (SR-HSI) images. ShuffleNet improved with convolution optimization and cross-stage partial architecture (ShuffleNet_COCSP) was used to build classification models with the optimal image set of effective wavelengths (OISEW) of SR-HSI images obtained by support vector machine and ShuffleNet. High-quality fusion of HSI and RGB with the obvious spatial promotion and satisfactory spectral conservation was gained by HRFN. ShuffleNet_COCSP and OISEW obtained the optimal recognition performance of ACCp=98.36%, Params=0.805 M, and FLOPs=0.097 G, outperforming other classification methods and other types of images. Overall, the proposed method provides an accurate and reliable identification of soybean kernel damages and would be extended to analysis of other quality indicators of various crop kernels.

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Section: Introductionmentioning

confidence: 99%

Fusion of hyperspectral imaging (HSI) and RGB for identification of soybean kernel damages using ShuffleNet with convolutional optimization and cross stage partial architecture

Zheng

Zhao²,

Zhu³

et al. 2023

Front. Plant Sci.

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“…holds a significant strategic position in the economic development of China. Rich in oil, protein, and various essential nutrients, soybean plays a prominent role in human and animal diets [ 1 , 2 ]. Currently, the capacity of soybean self-sufficiency is very low in China, with approximately 80–85% of soybeans being imported [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of Soybean Mutant Lines Based on Dual-Branch CNN Model Fusion Framework Utilizing Images from Different Organs

Lin

Deng

et al. 2023

Plants

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The accurate identification and classification of soybean mutant lines is essential for developing new plant varieties through mutation breeding. However, most existing studies have focused on the classification of soybean varieties. Distinguishing mutant lines solely by their seeds can be challenging due to their high genetic similarities. Therefore, in this paper, we designed a dual-branch convolutional neural network (CNN) composed of two identical single CNNs to fuse the image features of pods and seeds together to solve the soybean mutant line classification problem. Four single CNNs (AlexNet, GoogLeNet, ResNet18, and ResNet50) were used to extract features, and the output features were fused and input into the classifier for classification. The results demonstrate that dual-branch CNNs outperform single CNNs, with the dual-ResNet50 fusion framework achieving a 90.22 ± 0.19% classification rate. We also identified the most similar mutant lines and genetic relationships between certain soybean lines using a clustering tree and t-distributed stochastic neighbor embedding algorithm. Our study represents one of the primary efforts to combine various organs for the identification of soybean mutant lines. The findings of this investigation provide a new path to select potential lines for soybean mutation breeding and signify a meaningful advancement in the propagation of soybean mutant line recognition technology.

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“…Soybean (Glycine max L.) is an important industrial legume as human and animal feed in the world with an average of 18-22% oil, 38-56% vegetable protein in its seeds. Soybean is the most widely grown oilseed in the world as a main or second crop (Arslan et al, 2018). Soybean is originated from China.…”

Section: Introductionmentioning

confidence: 99%

“…Domestication and cultivation of soybean by humans in China date from about the 11 th century BC or a little earlier according to oldest records (Hymowitz and Shurtleff, 2005). The plant is introduced from the ancient world to the new world in the middle of the 17 th century and gained its worldwide importance at the beginning of the 20 th century (Arslan et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Morphological and Grain Yield Responses of Soybean (Glycine Max L.) to Time and Intensity of Defoliation Under Application of Amino Acid

Najafikhan-Behbin¹,

Dadashi²,

Faraji³

et al. 2019

Appl. Ecol. Env. Res.

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The use of amino acids (AA) to increase the uptake of nutrients has been increasing dramatically in recent years. This investigation was conducted in the two successive seasons of 2016 and 2017 on soybean in Golestan Province, Iran, to study the effect of time and intensity of defoliation on quantitative and qualitative characteristics of soybean (Glycine max L.) under the application of AA. A two-year factorial experiment was conducted based on randomized complete block design with three replications. The first factor included the defoliation intensity at three levels 0, 50 and 100%, the second factor included the defoliation time in five levels V1, V3, V5, V7, and R1 (growth stages), and the third factor was the application and non-application of AA. Results indicated that the effects of the defoliation time and intensity, as well as the foliar application of AA, were significant on morphological traits, yield and yield components, and oil and protein contents. In the quadruple interaction, the highest of grain yield (4040 kg.ha -1 ) was observed in the application of AA under defoliation in V5 with 50% intensity during the first year. The lowest value of this trait was achieved in non-application of AA under 100% defoliation in R1 growth stage during both years of the experiment (1585 and 1525 kg.ha -1 , respectively). The maximum grain oil and protein yield were observed in 100% defoliation in V7 growth stage under AA application during the first-year experiment (978.5 and 1499 kg.ha -1 , respectively). Also, the lowest value of the traits was observed in 100% defoliation in the V5 growth stage under non-application of AA during the second year (225.6 and 471.9 kg.ha -1 , respectively). According to the present study, soybean is regarded generally as a defoliation-tolerant crop. Also, foliar application of AA could be used as an effective amendment for preventing defoliation injury in yield and yield components of soybean.

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Assessment of Performances of Yield and Factors Affecting the Yield in Some Soybean Varieties/Lines Grown Under Semi-Arid Climate Conditions

Abstract: Arslan et al.: Assessment of performances of yield and factors affecting the yield in some soybean varieties/lines grown under semiarid climate conditions -4289 -

Cited by 4 publications

References 17 publications

Fusion of hyperspectral imaging (HSI) and RGB for identification of soybean kernel damages using ShuffleNet with convolutional optimization and cross stage partial architecture

Fusion of hyperspectral imaging (HSI) and RGB for identification of soybean kernel damages using ShuffleNet with convolutional optimization and cross stage partial architecture

Identification of Soybean Mutant Lines Based on Dual-Branch CNN Model Fusion Framework Utilizing Images from Different Organs

Morphological and Grain Yield Responses of Soybean (Glycine Max L.) to Time and Intensity of Defoliation Under Application of Amino Acid

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