An Approach Using Emerging Optical Technologies and Artificial Intelligence Brings New Markers to Evaluate Peanut Seed Quality

Tropical peanut maturation scale for harvesting seeds with superior quality

Determining the moment for harvesting the tropical peanut with a focus on superior seed quality is not an easy task. Particularities such as indeterminate flowering, underground fruiting and uneven maturation further increase this technical challenge. It is in this context that we aim to investigate harvest indicators based on the maturation and late maturation phases of tropical peanuts to obtain seeds with superior physiological and health quality. The plants were grown in field conditions and their development stages were carefully monitored until seed production. The water content, dry weight, germination capacity, desiccation tolerance, vigor, longevity, and seed pathogens were evaluated throughout these stages. We showed that seeds from early stages (R5 and R6) did not fully tolerate desiccation and were highly sensitive to pathogen contamination after storage (Aspergillus, Penicillium, and Bacteria). At late stages (R7, R8, and R9), the seeds had optimized vigor, longevity and bioprotection against fungi and thermal stress. The peanut maturation scale for tropical agriculture provides unique harvesting guidelines that make it possible to monitor the plants' development stages with a focus on producing superior quality seeds.

Multiomics Analyses Reveal the Dual Role of Flavonoids in Pigmentation and Abiotic Stress Tolerance of Soybean Seeds

The color of the seed coat has great diversity and is regarded as a biomarker of metabolic variations. Here we isolated a soybean variant (BLK) from a population of recombinant inbred lines with a black seed coat, while its sibling plants have yellow seed coats (YL). The BLK and YL plants showed no obvious differences in vegetative growth and seed weight. However, the BLK seeds had higher anthocyanins and flavonoids level and showed tolerance to various abiotic stresses including herbicide, oxidation, salt, and alkalinity during germination. Integrated metabolomic and transcriptomic analyses revealed that the upregulation of biosynthetic genes probably contributed to the overaccumulation of flavonoids in BLK seeds. The transient expression of those biosynthetic genes in soybean root hairs increased the levels of total flavonoids or anthocyanins. Our study revealed the molecular basis of flavonoid accumulation in soybean seeds, leveraging genetic engineering for both nutritious and stress-tolerant soybean germplasm.

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

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.

POMONA: a multiplatform software for modeling seed physiology

Seed physiology is related to functional and metabolic traits of the seed-seedling transition. In this sense, modeling the kinetics, uniformity and capacity of a seed sample plays a central role in designing strategies for trade, food, and environmental security. Thus, POMONA is presented as an easy-to-use multiplatform software designed to bring several logistic and linearized models into a single package, allowing for convenient and fast assessment of seed germination and or longevity, even if the data has a non-Normal distribution. POMONA is implemented in JavaScript using the Quasar framework and can run in the Microsoft Windows operating system, GNU/Linux, and Android-powered mobile hardware or on a web server as a service. The capabilities of POMONA are showcased through a series of examples with diaspores of corn and soybean, evidencing its robustness, accuracy, and performance. POMONA can be the first step for the creation of an automatic multiplatform that will benefit laboratory users, including those focused on image analysis.

Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality

The sanitary quality of seed is essential in agriculture. This is because pathogenic fungi compromise seed physiological quality and prevent the formation of plants in the field, which causes losses to farmers. Multispectral images technologies coupled with machine learning algorithms can optimize the identification of healthy peanut seeds, greatly improving the sanitary quality. The objective was to verify whether multispectral images technologies and artificial intelligence tools are effective for discriminating pathogenic fungi in tropical peanut seeds. For this purpose, dry peanut seeds infected by fungi (A. flavus, A. niger, Penicillium sp., and Rhizopus sp.) were used to acquire images at different wavelengths (365 to 970 nm). Multispectral markers of peanut seed health quality were found. The incubation period of 216 h was the one that most contributed to discriminating healthy seeds from those containing fungi through multispectral images. Texture (Percent Run), color (CIELab L*) and reflectance (490 nm) were highly effective in discriminating the sanitary quality of peanut seeds. Machine learning algorithms (LDA, MLP, RF, and SVM) demonstrated high accuracy in autonomous detection of seed health status (90 to 100%). Thus, multispectral images coupled with machine learning algorithms are effective for screening peanut seeds with superior sanitary quality.

A Reliable Method to Recognize Soybean Seed Maturation Stages Based on Autofluorescence-Spectral Imaging Combined With Machine Learning Algorithms

In recent years, technological innovations have allowed significant advances in the diagnosis of seed quality. Seeds with superior physiological quality are those with the highest level of physiological maturity and the integration of rapid and precise methods to separate them contributes to better performance in the field. Autofluorescence-spectral imaging is an innovative technique based on fluorescence signals from fluorophores present in seed tissues, which have biological implications for seed quality. Thus, through this technique, it would be possible to classify seeds in different maturation stages. To test this, we produced plants of a commercial cultivar (MG/BR 46 "Conquista") and collected the seeds at five reproductive (R) stages: R7.1 (beginning of maturity), R7.2 (mass maturity), R7.3 (seed disconnected from the mother plant), R8 (harvest point), and R9 (final maturity). Autofluorescence signals were extracted from images captured at different excitation/emission combinations. In parallel, we investigated physical parameters, germination, vigor and the dynamics of pigments in seeds from different maturation stages. To verify the accuracy in predicting the seed maturation stages based on autofluorescence-spectral imaging, we created machine learning models based on three algorithms: (i) random forest, (ii) neural network, and (iii) support vector machine. Here, we reported the unprecedented use of the autofluorescence-spectral technique to classify the maturation stages of soybean seeds, especially using the excitation/emission combination of chlorophyll a (660/700 nm) and b (405/600 nm). Taken together, the machine learning algorithms showed high performance segmenting the different stages of seed maturation. In summary, our results demonstrated that the maturation stages of soybean seeds have their autofluorescence-spectral identity in the wavelengths of chlorophylls, which allows the use of this technique as a marker of seed maturity and superior physiological quality.

Transcripts Expressed during Germination Sensu Stricto Are Associated with Vigor in Soybean Seeds

The rapid and uniform establishment of crop plants in the field underpins food security through uniform mechanical crop harvesting. In order to achieve this, seeds with greater vigor should be used. Vigor is a component of physiological quality related to seed resilience. Despite this importance, there is little knowledge of the association between events at the molecular level and seed vigor. In this study, we investigated the relationship between gene expression during germination and seed vigor in soybean. The expression level of twenty genes related to growth at the beginning of the germination process was correlated with vigor. In this paper, vigor was evaluated by different tests. Then we reported the identification of the genes Expansin-like A1, Xyloglucan endotransglucosylase/hydrolase 22, 65-kDa microtubule-associated protein, Xyloglucan endotransglucosylase/hydrolase 2, N-glycosylase/DNA lyase OGG1 and Cellulose synthase A catalytic subunit 2, which are expressed during germination, that correlated with several vigor tests commonly used in routine analysis of soybean seed quality. The identification of these transcripts provides tools to study vigor in soybean seeds at the molecular level.