Analysis and prediction of seed quality using machine learning

The mainstay of the economy has always been agriculture, and the majority of tasks are still carried out without the use of modern technology. Currently, the ability of human intelligence to forecast seed quality is used. Because it lacks a validation method, the existing seed prediction analysis is ineffective. Here, we have tried to create a prediction model that uses machine learning algorithms to forecast seed quality, leading to high crop yield and high-quality harvests. For precise seed categorization, this model was created using convolutional neural networks and trained using the seed dataset. Using data that can be used to forecast the future, this model is used to learn about whether the seeds are of premium quality, standard quality, or regular quality. While testing data are employed in the algorithm’s predictive analytics, training data and validation data are used for categorization reasons. Thus, by examining the training accuracy of the convolution neural network (CNN) model and the prediction accuracy of the algorithm, the project’s primary goal is to develop the best method for the more accurate prediction of seed quality

Hyperspectral imaging for the detection of plant pathogens in seeds: recent developments and challenges

Food security, a critical concern amid global population growth, faces challenges in sustainable agricultural production due to significant yield losses caused by plant diseases, with a multitude of them caused by seedborne plant pathogen. With the expansion of the international seed market with global movement of this propagative plant material, and considering that about 90% of economically important crops grown from seeds, seed pathology emerged as an important discipline. Seed health testing is presently part of quality analysis and carried out by seed enterprises and governmental institutions looking forward to exclude a new pathogen in a country or site. The development of seedborne pathogens detection methods has been following the plant pathogen detection and diagnosis advances, from the use of cultivation on semi-selective media, to antibodies and DNA-based techniques. Hyperspectral imaging (HSI) associated with artificial intelligence can be considered the new frontier for seedborne pathogen detection with high accuracy in discriminating infected from healthy seeds. The development of the process consists of standardization of methods and protocols with the validation of spectral signatures for presence and incidence of contamined seeds. Concurrently, epidemiological studies correlating this information with disease outbreaks would help in determining the acceptable thresholds of seed contamination. Despite the high costs of equipment and the necessity for interdisciplinary collaboration, it is anticipated that health seed certifying programs and seed suppliers will benefit from the adoption of HSI techniques in the near future

Determining the instantaneous bruising pattern in a sample potato tuber subjected to pendulum bob impact through finite element analysis

Potato bruising resulting from mechanical impact during production operations including harvest and postharvest is a significant concern within the potato production sector, leading to consumer complaints and economic losses. The detection of instantaneous internal bruising poses a particular challenge as it can progress over time during storage or transportation, making it difficult to identify immediately after external impact. This study aims to investigate the progression of bruising and accurately represent the instantaneous dynamic deformation behavior of potato tubers under four pendulum bob impact cases (pendulum arm angles of 30°, 45°, 60°, and 90°). To analyze the dynamic impact deformation characteristics of the tubers, solid modeling based on a reverse engineering approach and explicit dynamic engineering simulations were employed. The simulation results yielded valuable numerical data and visual representation of the deformation progression. The loading conditions considered in this study indicated that the maximum stress values, reaching 0.818 MPa at a pendulum arm angle of 90°, remained below the bio-yield stress point of the tuber flesh (1.05 MPa) determined through experimental compression tests. Therefore, it was concluded that the impact did not cause permanent deformation (i.e., permanent bruising) in the tuber. However, the numerical analysis clearly demonstrated the sequence of stress occurrences, which is a key contributing factor to potential permanent bruising. In this regard, the bruising energy threshold of 318.314 mJ (R2: 0.96) was extrapolated. The numerical findings presented in this study can aid in evaluating the susceptibility of tuber samples to bruising. By employing nonlinear explicit dynamics simulations, this research contributes to the advancement of understanding complex deformation and bruising in solid agricultural products. Moreover, the application of these techniques holds significant industrial implications for enhancing the handling and transportation of agricultural produce. Practical applications: This research aims to tackle the challenge of accurately representing the immediate internal bruising pattern in potato tubers resulting from mechanical impact. Conventional methods, such as physical or analytical expressions, may not fully capture the distribution of bruising experienced by the tubers. To overcome this limitation, an engineering simulation approach is proposed to provide a more precise depiction of the instantaneous bruising pattern. By advancing the understanding of complex deformation and bruising in solid agricultural products, this research contributes to improving the efficiency and quality of agricultural production in the industry. Additionally, this study offers a step-by-step guide on how to conduct these simulations effectively

Non-destructive assessment of vitamin C in foods: a review of the main findings and limitations of vibrational spectroscopic techniques

The constant increase in the demand for safe and high-quality food has generated the need to develop efficient methods to evaluate food composition, vitamin C being one of the main quality indicators. However, its heterogeneity and susceptibility to degradation makes the analysis of vitamin C difficult by conventional techniques, but as a result of technological advances, vibrational spectroscopy techniques have been developed that are more efficient, economical, fast, and non-destructive. This review focuses on main findings on the evaluation of vitamin C in foods by using vibrational spectroscopic techniques. First, the fundamentals of ultraviolet-visible, infrared and Raman spectroscopy are detailed. Also, chemometric methods, whose use is essential for a correct processing and evaluation of the spectral information, are described. The use and importance of vibrational spectroscopy in the evaluation of vitamin C through qualitative characterization and quantitative analysis is reported. Finally, some limitations of the techniques and potential solutions are described, as well as future trends related to the utilization of vibrational spectroscopic techniques

Unsupervised foreign object detection based on dual-energy absorptiometry in the food industry

X-ray imaging is a widely used technique for non-destructive inspection of agricultural food products. One application of X-ray imaging is the autonomous, in-line detection of foreign objects in food samples. Examples of such inclusions are bone fragments in meat products, plastic and metal debris in fish, and fruit infestations. This article presents a processing methodology for unsupervised foreign object detection based on dual-energy X-ray absorptiometry (DEXA). A novel thickness correction model is introduced as a pre-processing technique for DEXA data. The aim of the model is to homogenize regions in the image that belong to the food product and to enhance contrast where the foreign object is present. In this way, the segmentation of the foreign object is more robust to noise and lack of contrast. The proposed methodology was applied to a dataset of 488 samples of meat products acquired from a conveyor belt. Approximately 60% of the samples contain foreign objects of different types and sizes, while the rest of the samples are void of foreign objects. The results show that samples without foreign objects are correctly identified in 97% of cases and that the overall accuracy of foreign object detection reaches 95%

Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review

The quality of tropical fruits and vegetables and the expanding global interest in eating healthy foods have resulted in the continual development of reliable, quick, and cost-effective quality assurance methods. The present review discusses the advancement of non-destructive spectral measurements for evaluating the quality of major tropical fruits and vegetables. Fourier transform infrared (FTIR), Near-infrared (NIR), Raman spectroscopy, and hyperspectral imaging (HSI) were used to monitor the external and internal parameters of papaya, pineapple, avocado, mango, and banana. The ability of HSI to detect both spectral and spatial dimensions proved its efficiency in measuring external qualities such as grading 516 bananas, and defects in 10 mangoes and 10 avocados with 98.45%, 97.95%, and 99.9%, respectively. All of the techniques effectively assessed internal characteristics such as total soluble solids (TSS), soluble solid content (SSC), and moisture content (MC), with the exception of NIR, which was found to have limited penetration depth for fruits and vegetables with thick rinds or skins, including avocado, pineapple, and banana. The appropriate selection of NIR optical geometry and wavelength range can help to improve the prediction accuracy of these crops. The advancement of spectral measurements combined with machine learning and deep learning technologies have increased the efficiency of estimating the six maturity stages of papaya fruit, from the unripe to the overripe stages, with F1 scores of up to 0.90 by feature concatenation of data developed by HSI and visible light. The presented findings in the technological advancements of non-destructive spectral measurements offer promising quality assurance for tropical fruits and vegetables

Complexos Agroindustriais: Análise da Literatura Indexada na Base de Dados Web of Science – 1945 a 2020 / Agroindustrial Complexes: Analysis of Literature Indexed in the Web of Science Database – 1945 to 2020

O estudo objetivou analisar quantitativamente as publicações científicas em complexos agroindustriais a partir da literatura indexada na base de dados da Web of Science. De natureza exploratória-descritiva, empregou-se a técnica bibliométrica para análise da produção científica sobre a temática proposta. Utilizando-se os descritores “agro-industrial complex” ou “agroindustrial complex”, foram recuperados em fevereiro de 2021, 171 artigos. Os resultados apontaram Economics e Business Economics como categoria e área de pesquisa predominantes, respectivamente. O aumento no número de publicações mostrou-se ascendente ao longo do período com destaque para o ano de 2018. O inglês foi o idioma em que mais se publicou (57,9%), seguido do russo (28,07%). Quanto aos países de origem das publicações, destacaram-se Rússia, Ucrânia, Checoslováquia, Bielorrússia e Cazaquistão; destes, exceto Ucrânia e Checoslováquia, são membros da União Econômica Euroasiática (UEE). Das vinte instituições de pesquisa com maior volume de publicações em complexos agroindustriais, 60% estão situadas em território russo. Por fim, constatou-se um ordenamento com elevada dispersão das publicações, de modo que 21 pesquisadores concentram 44 publicações (5,80%) ao passo que 94,20% dos pesquisadores (341) possuem uma publicação cada

FOURIER TRANSFORM INFRARED SPECTROSCOPY (AS A RAPID METHOD) COUPLED WITH MACHINE LEARNING APPROACHES FOR DETECTION AND QUANTIFICATION OF GLUTEN CONTAMINATIONS IN GRAIN-BASED FOODS

Cross-contamination between food grains during harvesting, transportation, and/or food processing is still a major issue in the food industry. Due to cross-contact with gluten-rich grains (wheat, barley, and rye grains), gluten can get into food that’s naturally free from gluten and thus may not be safe for consumption for people susceptible to gluten-related disorders such as celiac disease, wheat allergy, gluten intolerance or sensitivity. The conventional method of gluten detection is cumbersome, time-consuming, and requires well-trained personnel. Therefore, there is a need for a rapid and equally effective technique to authenticate gluten contamination in foods. This research work explored the use of a Fourier transform infrared (FTIR) spectroscopy coupled with machine learning approaches to detect and quantify gluten contamination in grain-based foods. The research was divided into three different phases including the use of FTIR with supervised machine learning (ML) approaches to authenticate cross-contact between non-gluten and gluten flours, the use of FTIR with ML approaches to detect and quantify wheat flour contamination in gluten-free bread (cornbread), and finally, the use of Enzyme-linked immunosorbent assay (ELISA) as a complementary test to estimate and establish a gluten-free threshold of ≤ 20 ppm for the amount of gluten in wheat contaminated flour and cornbread. Different machine learning algorithms such as linear discriminant analysis (LDA), partial least square regression (PLSR), k-nearest neighbor (KNN), support vector machine, decision tree, and ensemble learning method were used for the development of ML models. The results obtained for the first phase of the research show that FTIR with LDA and PLSR has the potential to detect and quantify cross-contact between a non-gluten (corn flour, CF) and gluten-rich (wheat flour, WF, barley flour, BF, and rye flour, RF) flours, at contamination levels of 0.5% - 10% (w/w), with 0.5% increments. For the second phase, a majority voting-based ensemble learning (stack of random forest, k-nearest neighbor (KNN) and support vector classifier) model was able to detect WF contamination in a cornbread at the true-positive rate and false-negative rate of 1.0, respectively. The ELISA tests for both phases (the raw flour samples and the baked bread) showed a threshold limit of ≤ 0.5% contamination level for CF contaminated with WF to be labeled gluten-free and ≤ 3.5% for the cornbread contaminated with the WF to be gluten-free. This research is still in its development stage and has the potential to contribute towards artificial intelligence applications in ensuring food safety, and to food quality inspection

A review on nanomaterial-based SERS substrates for sustainable agriculture

The agricultural sector plays a pivotal role in driving the economy of many developing countries. Any dent in this economical structure may have a severe impact on a country's population. With rising climate change and increasing pollution, the agricultural sector is experiencing significant damage. Over time this cumulative damage will affect the integrity of food crops and create food security issues around the world. Therefore, an early warning system is needed to detect possible stress on food crops. Here we present a review of the recent developments in nanomaterial-based Surface Enhanced Raman Spectroscopy (SERS) substrates which could be utilized to monitor agricultural crop responses to natural and anthropogenic stress. Initially, our review delves into diverse and cost-effective strategies for fabricating SERS substrates, emphasizing their intelligent utilization across various agricultural scenarios. In the second phase of our review, we spotlight the specific application of SERS in addressing critical food security issues. By detecting nutrients, hormones, and effector molecules in plants, SERS provides valuable insights into plant health. Furthermore, our exploration extends to the detection of contaminants, chemicals, and foodborne pathogens within plants, showcasing the versatility of SERS in ensuring food safety. The cumulative knowledge derived from these discussions illustrates the transformative potential of SERS in bolstering the agricultural economy. By enhancing precision in nutrient management, monitoring plant health, and enabling rapid detection of harmful substances, SERS emerges as a pivotal tool in promoting sustainable and secure agricultural practices. Its integration into agricultural processes not only augments productivity but also establishes a robust defence against potential threats to crop yield and food quality. As SERS continues to evolve, its role in shaping the future of agriculture becomes increasingly pronounced, promising a paradigm shift in how we approach and address challenges in food production and safety

Light Transmission Properties of Lentil (Lens culinaris Medik.) Seed Coat and Effect of Light Exposure on Cotyledon Quality

Cotyledon color is one of the most important quality criteria in the lentil market because the color may correlate well with other quality attributes. Therefore, cotyledon color is an important quality criterion in lentil breeding programs. The objectives of this work were to investigate the variation in optical properties among lentil seed coat types and to determine the effect of light treatment, seed coat presence, and seed coat type on color loss in lentil cotyledon. Light transmission properties of seed coat types were obtained to find out if they differ in their light-blocking ability and protection of the underlying cotyledon from photodegradation. Light reflectivity was measured to investigate if there are recognizable patterns, which might be useful in market class discrimination, quality prediction and disease detection in the seeds. A fiber-optic spectrometer was used to obtain spectral reflectivity and transmission properties of seed coats of 20 lentil genotypes. The reflectivity (0°\32°) and nadir-aligned transmission spectra were measured in the 250 nm to 850 nm wavelength range. An Analysis of Variance (ANOVA) showed that there were significant (p<0.05) differences in light transmission properties of the major seed coat types. A computer vision system was used to study the influence of light exposure on the cotyledon color of red, green, and yellow lentils. Twenty samples from each of the three cotyledon color classes were subjected to six levels of light treatment, namely ultraviolet, full-spectrum visible, red, green, blue, and control (dark) for seven days, at room temperature. This light exposure had a significant effect on all three cotyledon color classes. The effect size was largest in green lentils, smaller in yellow, and least in red lentils. Having established the light-blocking characteristics of the various seed coats and realizing that light exposure does affect the color of lentil cotyledon, the protective effects of different kinds of seed coat against light-induced cotyledon color change was tested. Results showed that some whole green cotyledon lentils experienced color losses in the underlying cotyledon. Red and yellow lentil classes had high levels of colorfastness, and their seed coats successfully protected the cotyledon from these minimal effects. Thus, breeding for seed coat protection may not improve the cotyledon color of Canadian red lentils (the most de-hulled market class), but it may improve the overall quality of green lentils