Vigour testing for the rice seed with computer vision-based techniques

Abstract

Rice is the staple food for approximately half of the world’s population. Seed vigour has a crucial impact on the yield, which can be evaluated by germination rate, vigor index and etc. Existing seed vigour testing methods heavily rely on manual inspections that are destructive, time-consuming, and labor-intensive. To address the drawbacks of existing rice seed vigour testing, we proposed a multispectral image-based non-destructive seed germination testing approach. Specifically, we collected multispectral data in 19 wavebands for six rice varieties. Furthermore, we designed an end-to-end pipeline, denoted as MsiFormer (MisFormer cod3e will be available at https://github.com/LiaoYun0x0/MisFormer) by integrating a Yolo-based object detector (trained Yolo v5) and a vision transformer-based vigour testing model, which effectively improved the automation and efficiency of existing techniques. In order to objectively evaluate the performance of the proposed method in this paper, we conduct a comparison between MisFormer and other 3 deep learning methods. The results showed that, MisFormer performed much better with the accuracy of 94.17%, which was 2.5%-18.34% higher than the other 3 deep learning methods. Besides MsiFormer, possibilities of CIELab mediated image analysis of TTC (tetrazolium chloride) staining in rice seed viability and nCDA (normalized canonical discriminant analysis) in rice seed vigour were also discussed, where CIELab L* of TTC staining were negatively correlated with vigor index and germination rate, with Pearson’s correlation coefficient of -0.9874, -0.9802 respectively, and CIELab A* of TTC staining were and positively correlated with vigor index and germination rate, with Pearson’s correlation coefficient of 0.9624, 0.9544 respectively, and CIELab A* of nCDA had Pearson’s correlation coefficient of -0.8866 and -0.9340 with vigor index and germination rate, respectively. Besides testing methods, vigour results within and among variety(ies) showed that, there were great variations among the 6 rice varieties, and mean coefficient of variation (CV) of vigor index of individual seed within a variety reached 64.87%, revealing the high risk of conventional methods in random sampling. Vigour variations had close relationship with wavelengths of 780 nm-970 nm, indicating their value in future research.

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

“…However, it relied on destructive tests. In a vigor test for rice seeds using computer vision techniques [26], a new prediction model was introduced for non-destructive germination forecasting, achieving a high accuracy of 94.17%. This demonstrates that our results outperform other deep learning methods.…”

Applying YOLO-V8 and X-ray Morphology Analysis to Assess the Vigor of Brachiaria brizantha cv. Xaraés Seeds

“…However, it relied on destructive tests. In a vigor test for rice seeds using computer vision techniques [16], a new prediction model was introduced for nondestructive germination forecasting, achieving a high accuracy of 94.17%. This demonstrates that our results outperform other deep learning methods.…”

“…In this context, Computer Vision techniques have been widely applied in digital agriculture. Recent applications range from seed vigor testing [15,16] to precision beekeeping [17,18]. Moreover, recent progress in Deep Learning, especially convolutional neural networks such as YOLO [20] and R-CNN, has garnered considerable attention for their capability to identify occurrences in various regions of images [6][7][8].…”

Nitric Oxide Promoted the Seed Germination of Cynanchum auriculatum under Cadmium Stress

“…Additionally, in this study, each treatment included 3 biological replicates consisting of 20 seeds. Triphenyl tetrazolium chloride (TTC) was used to evaluate seed viability in petri dishes in addition to the germination experiment [36].…”