Optical sensors combined with machine learning algorithms have led to significant advances in seed science. These advances have facilitated the development of robust approaches, providing ...decision-making support in the seed industry related to the marketing of seed lots. In this study, a novel approach for seed quality classification is presented. We developed classifier models using Fourier transform near-infrared (FT-NIR) spectroscopy and X-ray imaging techniques to predict seed germination and vigor. A forage grass (Urochloa brizantha) was used as a model species. FT-NIR spectroscopy data and radiographic images were obtained from individual seeds, and the models were created based on the following algorithms: linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), random forest (RF), naive Bayes (NB), and support vector machine with radial basis (SVM-r) kernel. In the germination prediction, the models individually reached an accuracy of 82% using FT-NIR data, and 90% using X-ray data. For seed vigor, the models achieved 61% and 68% accuracy using FT-NIR and X-ray data, respectively. Combining the FT-NIR and X-ray data, the performance of the classification model reached an accuracy of 85% to predict germination, and 62% for seed vigor. Overall, the models developed using both NIR spectra and X-ray imaging data in machine learning algorithms are efficient in quickly, non-destructively, and accurately identifying the capacity of seed to germinate. The use of X-ray data and the LDA algorithm showed great potential to be used as a viable alternative to assist in the quality classification of U. brizantha seeds.
In the agricultural industry, advances in optical imaging technologies based on rapid and non-destructive approaches have contributed to increase food production for the growing population. The ...present study employed autofluorescence-spectral imaging and machine learning algorithms to develop distinct models for classification of soybean seeds differing in physiological quality after artificial aging. Autofluorescence signals from the 365/400 nm excitation-emission combination (that exhibited a perfect correlation with the total phenols in the embryo) were efficiently able to segregate treatments. Furthermore, it was also possible to demonstrate a strong correlation between autofluorescence-spectral data and several quality indicators, such as early germination and seed tolerance to stressful conditions. The machine learning models developed based on artificial neural network, support vector machine or linear discriminant analysis showed high performance (0.99 accuracy) for classifying seeds with different quality levels. Taken together, our study shows that the physiological potential of soybean seeds is reduced accompanied by changes in the concentration and, probably in the structure of autofluorescent compounds. In addition, altering the autofluorescent properties in seeds impact the photosynthesis apparatus in seedlings. From the practical point of view, autofluorescence-based imaging can be used to check modifications in the optical properties of soybean seed tissues and to consistently discriminate high-and low-vigor seeds.
Conventional methods for detecting seed-borne fungi are laborious and time-consuming, requiring specialized analysts for characterization of pathogenic fungi on seed. Multispectral imaging (MSI) ...combined with machine vision was used as an alternative method to detect
(Eidam) Sharif
(Eidam) in black oat seeds (
Schreb). The seeds were inoculated with
(
) and then incubated for 24, 72 and 120 h. Multispectral images of non-infested and infested seeds were acquired at 19 wavelengths within the spectral range of 365 to 970 nm. A classification model based on linear discriminant analysis (LDA) was created using reflectance, color, and texture features of the seed images. The model developed showed high performance of MSI in detecting
in black oat seeds, particularly using color and texture features from seeds incubated for 120 h, with an accuracy of 0.86 in independent validation. The high precision of the classifier showed that the method using images captured in the Ultraviolet A region (365 nm) could be easily used to classify black oat seeds according to their health status, and results can be achieved more rapidly and effectively compared to conventional methods.
•Optical sensors allow real-time diagnosis of the hormetic effects of low-dose gamma radiation.•The reflectance, autofluorescence, texture and color features of soybean seeds are altered by low-dose ...60Co gamma rays.•Soybean seeds irradiated with very low dose of gamma radiation show greater reflectance at 690 nm.•Seedlings from irradiated seeds present altered initial fluorescence due to hormesis phenomena.
The use of radiation technology in agriculture has increased remarkably in recent years. The stimulating effect of low-dose gamma rays, known as hormesis, improves the adaptive response of living organisms to more severe challenges. Different areas of agriculture such as plant physiology, crop breeding and seed biology demand accurate methods for real-time diagnosis of the ionizing radiation effect on biological systems. However, traditional measures are still calculated from slow, laborious and destructive techniques, which depend on human vision. Here, we present two robust optical sensors based on multispectral and fluorescence imaging approaches for rapid and non-destructive detection of the hormetic effects of low-dose gamma radiation in soybean seeds and seedings. Multispectral images were acquired from seeds treated with gamma radiation (0, 12, 16 and 20 Gy), and the reflectance, autofluorescence, texture and color features were extracted from images. Next, a chlorophyll fluorescence-based technique was applied to measure the minimal level of fluorescence (F0), maximum fluorescence (FM), average chlorophyll a fluorescence and quantum yield of photosystem II (FV/FM) in seedlings grown from irradiated and non-irradiated seeds. Using a reflectance technology, the seedlings were also evaluated for anthocyanin, chlorophyll a and normalized difference vegetation index (NDVI). Results showed that multispectral and fluorescence-based techniques provide useful information on the hormetic effects of gamma rays in soybean seeds and seedlings, which are invisible to the naked eye. Our findings provide a better understanding of the link between spectral and fluorescence parameters and the hormetic effect of low-dose gamma rays. From a practical point of view, such optical sensors can provide markers for fast, non-destructive and reliable identification of the best gamma-ray doses to stimulate soybean growth parameters. However, we strongly support in-depth studies of the dose–response relationship in seeds with different initial physiological potential from different soybean genotypes.
The sterile insect technique (SIT) has been widely used to suppress several fruit fly species. In southern Brazil, millions of sterile flies of the South American fruit fly, Anastrepha fraterculus ...Wiedemann (Dipetra: Tephritidae), will be produced in a mass‐rearing facility called MOSCASUL to suppress wild populations from commercial apple orchards. In spite of standard rearing conditions, the quality of pupal batches can be inconsistent due to various factors. The quantification of poor quality material (e.g. empty pupae, dead pupae or larvae) is necessary to track down rearing issues, and pupal samples must be taken randomly and evaluated individually. To speed up the inspection of pupal samples by replacing the manual testing with the mechanized one, this study assessed a multispectral imaging (MSI) system to distinguish the variations in quality of A. fraterculus pupae and to quantify the variations based on reflectance patterns. Image acquisition and analyses were performed by the VideometerLab4 system on 7‐d‐old pupae by using 19 wavelengths ranging from 375 to 970 nm. The image representing the near infrared wavelength of 880 nm clearly distinguished among high‐quality pupae and the other four classes (i.e. low‐quality pupae, empty pupae, dead pupae and larvae). The blind validation test indicated that the MSI system can classify the fruit fly pupae with high accuracy. Therefore, MSI‐based classification of A. fraterculus pupae can be used for future pupal quality assessments of fruit flies in mass‐rearing facilities.
The application of artificial intelligence (AI) such as deep learning in the quality control of grains has the potential to assist analysts in decision making and improving procedures. Advanced ...technologies based on X-ray imaging provide markedly easier ways to control insect infestation of stored products, regardless of whether the quality features are visible on the surface of the grains. Here, we applied contrast enhancement algorithms based on peripheral equalization and calcification emphasis on X-ray images to improve the detection of
in maize grains. In addition, we proposed an approach based on convolutional neural networks (CNNs) to identity non-infested and infested classes using three different architectures; (i) Inception-ResNet-v2, (ii) Xception and (iii) MobileNetV2. In general, the prediction models developed based on the MobileNetV2 and Xception architectures achieved higher accuracy (≥0.88) in identifying non-infested grains and grains infested by maize weevil, with a correct classification from 0.78 to 1.00 for validation and test sets. Hence, the proposed approach using enhanced radiographs has the potential to provide precise control of
for safe human consumption of maize grains. The proposed method can automatically recognize food contaminated with hidden storage pests without manual features, which makes it more reliable for grain inspection.
The use of non-destructive methods with less human interference is of great interest in agricultural industry and crop breeding. Modern imaging technologies enable the automatic visualization of ...multi-parameter for characterization of biological samples, reducing subjectivity and optimizing the analysis process. Furthermore, the combination of two or more imaging techniques has contributed to discovering new physicochemical tools and interpreting datasets in real time.
We present a new method for automatic characterization of seed quality based on the combination of multispectral and X-ray imaging technologies. We proposed an approach using X-ray images to investigate internal tissues because seed surface profile can be negatively affected, but without reaching important internal regions of seeds. An oilseed plant (Jatropha curcas) was used as a model species, which also serves as a multi-purposed crop of economic importance worldwide. Our studies included the application of a normalized canonical discriminant analyses (nCDA) algorithm as a supervised transformation building method to obtain spatial and spectral patterns on different seedlots. We developed classification models using reflectance data and X-ray classes based on linear discriminant analysis (LDA). The classification models, individually or combined, showed high accuracy (> 0.96) using reflectance at 940 nm and X-ray data to predict quality traits such as normal seedlings, abnormal seedlings and dead seeds.
Multispectral and X-ray imaging have a strong relationship with seed physiological performance. Reflectance at 940 nm and X-ray data can efficiently predict seed quality attributes. These techniques can be alternative methods for rapid, efficient, sustainable and non-destructive characterization of seed quality in the future, overcoming the intrinsic subjectivity of the conventional seed quality analysis.
Manual monitoring of animal behavior is time-consuming and prone to bias. An alternative to such limitations is using computational resources in behavioral assessments, such as tracking systems, to ...facilitate accurate and long-term evaluations. There is a demand for robust software that addresses analysis in heterogeneous environments (such as in field conditions) and evaluates multiple individuals in groups while maintaining their identities. The Ethoflow software was developed using computer vision and artificial intelligence (AI) tools to monitor various behavioral parameters automatically. An object detection algorithm based on instance segmentation was implemented, allowing behavior monitoring in the field under heterogeneous environments. Moreover, a convolutional neural network was implemented to assess complex behaviors expanding behavior analyses' possibilities. The heuristics used to generate training data for the AI models automatically are described, and the models trained with these datasets exhibited high accuracy in detecting individuals in heterogeneous environments and assessing complex behavior. Ethoflow was employed for kinematic assessments and to detect trophallaxis in social bees. The software was developed in desktop applications and had a graphical user interface. In the Ethoflow algorithm, the processing with AI is separate from the other modules, facilitating measurements on an ordinary computer and complex behavior assessing on machines with graphics processing units. Ethoflow is a useful support tool for applications in biology and related fields.
Light-based methods are being further developed to meet the growing demands for food in the agricultural industry. Optical imaging is a rapid, non-destructive, and accurate technology that can ...produce consistent measurements of product quality compared to conventional techniques. In this research, a novel approach for seed quality prediction is presented. In the proposed approach two advanced optical imaging techniques based on chlorophyll fluorescence and chemometric-based multispectral imaging were employed. The chemometrics encompassed principal component analysis (PCA) and quadratic discrimination analysis (QDA). Among plants that are relevant as both crops and scientific models, tomato, and carrot were selected for the experiment. We compared the optical imaging techniques to the traditional analytical methods used for quality characterization of commercial seedlots. Results showed that chlorophyll fluorescence-based technology is feasible to discriminate cultivars and to identify seedlots with lower physiological potential. The exploratory analysis of multispectral imaging data using a non-supervised approach (two-component PCA) allowed the characterization of differences between carrot cultivars, but not for tomato cultivars. A Random Forest (RF) classifier based on Gini importance was applied to multispectral data and it revealed the most meaningful bandwidths from 19 wavelengths for seed quality characterization. In order to validate the RF model, we selected the five most important wavelengths to be applied in a QDA-based model, and the model reached high accuracy to classify lots with high-and low-vigor seeds, with a correct classification from 86 to 95% in tomato and from 88 to 97% in carrot for validation set. Further analysis showed that low quality seeds resulted in seedlings with altered photosynthetic capacity and chlorophyll content. In conclusion, both chlorophyll fluorescence and chemometrics-based multispectral imaging can be applied as reliable proxies of the physiological potential in tomato and carrot seeds. From the practical point of view, such techniques/methodologies can be potentially used for screening low quality seeds in food and agricultural industries.
New computer vision solutions combined with artificial intelligence algorithms can help recognize patterns in biological images, reducing subjectivity and optimizing the analysis process. The aim of ...this study was to propose an approach based on interactive and traditional machine learning methods to classify soybean seeds and seedlings according to their appearance and physiological potential. In addition, we correlated the appearance of seeds to their physiological performance. Images of soybean seeds and seedlings were used to develop models using low-cost approaches and free-access software. The models developed showed high performance, with overall accuracy reaching 0.94 for seeds and seedling classification. The high precision of the models that were developed based on interactive and traditional machine learning demonstrated that the method can easily be used to classify soybean seeds according to their appearance, as well as to classify soybean seedling vigor quickly and non-subjectively. The appearance of soybean seeds is strongly correlated with their physiological performance.
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