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

A novel approach integrating multispectral imaging and machine learning to identify seed maturity and vigor in smooth bromegrass

Smooth bromegrass (Bromus inermis) was adopted as experiment materials for identifying the seed maturity using a combination of multispectral imaging and machine learning. The trials were conducted to investigate the effects of three nitrogen application levels (0, 100 and 200 kg N ha- 1, defined as CK, N1 and N2 respectively) and two spikelet grain positions: superior grain (SG) at the basal position and inferior grain (IG) at the upper position, on smooth bromegrass seeds. The germination characteristics of the seeds revealed that the variations in nitrogen application and grain positions significantly influenced seeds vigor. The seed vigor of increased gradually with their maturity, reaching a high level at 30 and 36 days after anthesis. A stacking ensemble learning approach was employed to identify the seed maturity based on multispectral imaging and autofluorescence imaging. The results demonstrated that the Ensemble model outperformed Support Vector Machine, Bayesian, XGBoost and Random Forest across all evaluated metrics in different scenarios. The model accuracy in CK, N1 and N2 were 89%, 87% and 93%, respectively. Furthermore, the SHapley Additive exPlanations method was selected to interpret the Ensemble model, identifying important features such as 405, 430, 540, 630, 645, 690, 850, 880 and 970 nm. These features exhibited a significant correlation with fresh weight, shoot length and vigor index. These findings showed the high accuracy and generalizability of the Ensemble model for identifying the maturity and quality of smooth bromegrass seeds. Therefore, a new strategy would be offered for evaluating seed maturity and vigor level.

YOLOv8-licorice: a lightweight salt-resistance detection method for licorice based on seed germination state

Seeds will display different germination states during the germination process, and their good or bad state directly influences the subsequent growth and yield of the crop. This study aimed to address the difficulties of obtaining the images of seed germination process in all time series and studying the dynamic evolution law of seed germination state under stress conditions. A licorice sprouting experiment was performed using a seed sprouting phenotype acquisition system to obtain images of the sprouting process of licorice in full-time sequence. A labeled dataset of licorice full-time sequence sprouting process images was constructed based on the four states of unsprouted, sprouted, cracked, and shelled in the sprouting process. An optimized model, YOLOv8-Licorice, was developed based on the YOLOv8-n model and its effectiveness was demonstrated by comparative and ablation tests. Different salt stress environments were simulated via NaCl aqueous solution concentration, and germination experiments of licorice seeds were performed under different salt stresses. The germination state of licorice under different salt stress environments was detected using the YOLOv8-Licorice detection model. Percentage curve of licorice seeds in an unsprouted state displayed a continuous decreasing trend. For the percentage curve of licorice seeds in the sprouted state, an increasing and then decreasing trend was observed under the condition of 0-200 mmol/L NaCl solution, and a continuous increasing trend was observed under the condition of 240-300 mmol/L NaCl solution. Licorice seeds in the cracked state demonstrated percentage curves with an increasing and then decreasing trend under the condition of 0-140 mmol/L NaCl solution and a continuous increasing trend under the condition of 160-300 mmol/L NaCl solution. The percentage curve of licorice seeds in shelled state displayed a continuous increasing trend in 0-200 mmol/L NaCl solution condition and remained horizontal in 220-300 mmol/L NaCl solution condition. Overall, this study provides a valuable method involving the seed sprouting phenotype acquisition system and the proposed method for detecting the germination state of licorice seeds. This method serves as a valuable reference to comprehensively understand the seed sprouting process under triggering treatment.

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.

Integrating optical imaging techniques for a novel approach to evaluate Siberian wild rye seed maturity

Advances in optical imaging technology using rapid and non-destructive methods have led to improvements in the efficiency of seed quality detection. Accurately timing the harvest is crucial for maximizing the yield of higher-quality Siberian wild rye seeds by minimizing excessive shattering during harvesting. This research applied integrated optical imaging techniques and machine learning algorithms to develop different models for classifying Siberian wild rye seeds based on different maturity stages and grain positions. The multi-source fusion of morphological, multispectral, and autofluorescence data provided more comprehensive information but also increases the performance requirements of the equipment. Therefore, we employed three filtering algorithms, namely minimal joint mutual information maximization (JMIM), information gain, and Gini impurity, and set up two control methods (feature union and no-filtering) to assess the impact of retaining only 20% of the features on the model performance. Both JMIM and information gain revealed autofluorescence and morphological features (CIELab A, CIELab B, hue and saturation), with these two filtering algorithms showing shorter run times. Furthermore, a strong correlation was observed between shoot length and morphological and autofluorescence spectral features. Machine learning models based on linear discriminant analysis (LDA), random forests (RF) and support vector machines (SVM) showed high performance (>0.78 accuracies) in classifying seeds at different maturity stages. Furthermore, it was found that there was considerable variation in the different grain positions at the maturity stage, and the K-means approach was used to improve the model performance by 5.8%-9.24%. In conclusion, our study demonstrated that feature filtering algorithms combined with machine learning algorithms offer high performance and low cost in identifying seed maturity stages and that the application of k-means techniques for inconsistent maturity improves classification accuracy. Therefore, this technique could be employed classification of seed maturity and superior physiological quality for Siberian wild rye seeds.