Work

Rapid and Non-Destructive Detection of Soybean Seed Viability Using Temporally Enhanced Speckle Features Coupled with Lightweight Attention Network

Year: 2026

Type: article

Abstract: Soybean seed viability is critical for field emergence, stand uniformity, and yield stability, whereas conventional assessment methods are often destructive, time-consuming, and labor-intensive. This study developed a rapid and non-destructive soybean seed viability identification framework centered on a temporally enhanced GraphAVD representation, together with a lightweight channel-aware classifier for downstream discrimination. A class- and cultivar-balanced dataset of 3000 soybean seeds with unambiguous binary labels was constructed, with each of the five cultivars contributing 300 germinated untreated viable seeds and 300 non-germinated heat-treated non-viable seeds. The dataset was divided into a development set (n = 2500) and a fully isolated independent test set (n = 500). Dynamic speckle sequences were converted into feature maps using a temporally enhanced GraphAVD method (TD-GraphAVD), which incorporates multi-step temporal differencing, exponentially decaying weighting, and overlapping sliding-window aggregation to more systematically exploit temporal dynamic information in laser speckle sequences. Under the same ResNet-50 framework, TD-GraphAVD achieved the best feature-extraction performance among the compared methods, with a best validation Accuracy of 84.80% and a best validation Macro-F1 of 84.80%. Under TD-GraphAVD feature input, EfficientNet-B0 enhanced with an Efficient Channel Attention module achieved the strongest downstream classification performance, with a best validation Accuracy and Macro-F1 of 95.87% on the fixed internal validation split. Across the 10-fold cross-validation procedure on the development set, the model achieved mean validation Accuracy and Macro-F1 of 94.85% and 94.85%, respectively. On the independent test set, the mean Accuracy and Macro-F1 of the fold-trained sub-models remained 92.26% and 92.25%, respectively. These results indicate that the proposed framework is effective for rapid and non-destructive soybean seed viability discrimination under the present heat-inactivation-based laboratory-controlled binary setting, while its applicability to naturally aged, partially deteriorated, or otherwise heterogeneous non-viable seed populations remains to be further verified.

Source: Agriculture

DOI: 10.3390/agriculture16101026

Authors: Sen Men, Junhao Zhang, Lei Ni, Qinghua Tian, Xiaoming Jin

Institution: Beijing Union University

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $3,137