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RGB imaging segmentation procedure and processed spectra. (a) Broccoli sample placed on a black plate. (b) Segmented broccoli head. (c) Segmented broccoli crown without stem. (d) Reflectance measured by HinaLea 4200 hyperspectral camera. (e) Preprocessed spectra. The colors of spectra in (d-e) correspond to the colored points in (a).
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Fresh fruit and vegetables are invaluable for human health; however, their quality often deteriorates before reaching consumers due to ongoing biochemical processes and compositional changes. We currently lack any objective indices which indicate the freshness of fruit or vegetables resulting in limited capacity to improve product quality eventuall...
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Fresh fruits and vegetables are invaluable for human health, but their quality deteriorates before reaching consumers due to ongoing biochemical processes and compositional changes. The current lack of any objective indices for defining “ freshness ” of fruits or vegetables limits our capacity to control product quality leading to food loss and was...
Citations
... By employing supervised learning techniques, researchers can categorize different cultivars of Brassica species according to their glucosinolate profiles, which is essential for both breeding and consumer preferences [137]. ML algorithms are capable of analyzing spectral data from methods such as hyperspectral imaging to classify plant materials rapidly and non-destructively, providing a valuable tool for quality control in agricultural practices [139]. This classification capability can also extend to identifying plant varieties with enhanced health-promoting properties, thereby guiding breeding programs aimed at improving nutritional quality [137]. ...
Citation: Li, X.; Wen, D.; He, Y.; Liu, Y.; Han, F.; Su, J.; Lai, S.; Zhuang, M.; Gao, F.; Li, Z. Progresses and Prospects on Glucosinolate Detection in Cruciferous Plants. Foods 2024, 13, 4141. https://doi.org/10.3390/ foods13244141 Academic Editors: Abstract: This review provides a comprehensive summary of the latest international research on detection methods for glucosinolates in cruciferous plants. This article examines various analytical techniques , including high-performance liquid chromatography (HPLC), liquid chromatography-mass spectrometry (LC-MS), enzyme-linked immunosorbent assay (ELISA), and capillary electrophoresis (CE), while highlighting their respective advantages and limitations. Additionally, this review delves into recent advancements in sample preparation, extraction, and quantification methods, offering valuable insights into the accurate and efficient determination of glucosinolate content across diverse plant materials. Furthermore, it underscores the critical importance of the standardization and validation of these methodologies to ensure reliable glucosinolate analyses in both scientific research and industrial applications.
... By employing supervised learning techniques, researchers can categorize different cultivars of Brassica species according to their glucosinolate profiles, which is essential for both breeding and consumer preferences [95]. ML algorithms are capable of analyzing spectral data from methods such as hyperspectral imaging to classify plant materials rapidly and non-destructively, providing a valuable tool for quality control in agricultural practices [98]. This classification capability can also extend to identifying plant varieties with enhanced healthpromoting properties, thereby guiding breeding programs aimed at improving nutritional quality [95]. ...
This review provides a comprehensive summary of the latest international research on detection methods for glucosinolates in cruciferous plants. The article examines various analytical tech-niques, including high-performance liquid chromatography (HPLC), liquid chromatog-raphy-mass spectrometry (LC-MS), and enzyme-linked immunosorbent assay (ELISA), while highlighting their respective advantages and limitations. Additionally, the review delves into recent advancements in sample preparation, extraction, and quantification methods, offering valuable insights into the accurate and efficient determination of glucosinolate content across diverse plant materials. Furthermore, it underscores the critical importance of standardization and validation of these methodologies to ensure reliable glucosinolate analysis in both scientific research and industrial applications.
... In fruit, it has been used for evaluating the internal quality of apples [6], measuring maturity in strawberries [7], predicting yield and estimating nutrient concentrations in strawberries [8], and determining the ripening time in avocados [9], as well as for early decay detection [10]. In vegetables, it has been used to evaluate postharvest broccoli freshness [11], determine the accumulation of anthocyanins in bok choy [12], determine the moisture content of rapeseed leaves [13], Horticulturae 2024, 10, 802 2 of 8 and classify cabbage blight infection [14], as well as for the detection of Escherichia coli contamination in packaged fresh spinach [15]. ...
Domestic export cut lily flowers are expensive in Japan when they are in bud state that has not yet bloomed and when no leaf yellowing has occurred. Predicting the blooming day of domestic cut lily flowers is essential to increase their commodity value. Thermal imaging, spectroscopic technologies, and hyperspectral cameras have recently been used for quality prediction. This study uses a hyperspectral camera, reflectance of wavelength, and a support vector machine (SVM) to evaluate the predictability of blooming days of cut lily flowers. While examining spectra at wavelengths of 750–900 nm associated with pollination, the resultant reflectance was over 75% during six to four days before blooming and 30% on a blooming day, indicating a decline in their reflectance toward blooming. Furthermore, SVM classification models based on kernel function revealed that the quadratic SVM had the highest accuracy at 84.4%, while the coarse Gaussian SVM had the lowest accuracy at 34.4%. The most crucial wavelength for the quadratic SVM was 842.3 nm, which was associated with water. The quadratic SVM’s accuracy, verified using the area under the curve (ACU), was above 0.8, showing suitability for spectral classification based on blooming day prediction. Thus, this study shows that hyperspectral imaging can classify spectra based on the blooming day, indicating its potential to predict the blooming day, vase life, and quality of cut lily flowers.
... Existing researches shows that most of the current methods for automatic detection of vegetable and fruit freshness are based on feature engineering, that is, feature extraction is performed on images of vegetables and fruits of different freshness, and then machine learning methods are used to detect the freshness of vegetables and fruits according to the extracted features (Altaheri et al., 2019;Guo et al., 2022;X. Y. Huang et al., 2019;Koyama et al., 2021;Sarkar et al., 2022;Zhang et al., 2019). ...
Vegetable and fruit freshness detecting can ensure that consumers get vegetables and fruits with good taste and rich nutrition, improve the health level of diet, and ensure that the agricultural and food industries provide high-quality products to meet consumer needs and increase sales and market share. At present, the freshness detection of vegetables and fruits mainly relies on manual observation and judgment, which has the problems of subjectivity and low accuracy, and it is difficult to meet the needs of large-scale, high-efficiency, and rapid detection. Although some studies have shown that large-scale detection of vegetable and fruit freshness can be carried out based on artificially extracted features, there is still the problem of poor adaptability of artificially extracted features, which leads to low efficiency of freshness detection. To solve this problem, this paper proposes a novel method for detecting the freshness of vegetables and fruits more objectively, accurately and efficiently using deep features extracted by pre-trained deep learning models of different architectures. First, resized images of vegetables and fruits are fed into a pre-trained deep learning model for deep feature extraction. Then, the deep features are fused and the fused deep features are dimensionally reduced to a representative low-dimensional feature space by principal component analysis. Finally, vegetable and fruit freshness are detected by three machine learning methods. The experimental results show that combining the deep features extracted by the three architecture pre-trained deep learning models GoogLeNet, DenseNet-201 and ResNeXt-101 combined with PCA dimensionality reduction processing has achieved the highest accuracy rate of 96.98% for vegetable and fruit freshness detection. This research concluded that the proposed method is promising to improve the efficiency of freshness detection of vegetables and fruits.
... UAV swarms are equipped with remote optical cameras and sensors to provide high-resolution images for appropriate disease diagnosis and monitoring of Brassica plants. The widely utilized sensors are RGB (red, green, and blue), multispectral, hyperspectral, infrared thermal (IRT), and fluorescence imaging cameras [7]. However, in some cases, UAV-generated images are blurred and may contain noisy backgrounds. ...
... These sensors provide more spectral band information with higher spectral resolution, therefore, help in distinguishing the variation in spectral traits among different plants and diseases [44]. In [7], hyperspectral images of broccoli provided quantitative parameters for detecting glucosinolates levels. In [45], two cameras were used for estimating vegetable crop biomass. ...
Technological advances in unmanned aerial vehicles (UAVs) pursued by artificial intelligence (AI) are improving remote sensing applications in smart agriculture. These are valuable tools for monitoring and disease identification of plants as they can collect data with no damage and effects on plants. However, their limited carrying and battery capacities restrict their performance in larger areas. Therefore, using multiple UAVs, especially in the form of a swarm is more significant for monitoring larger areas such as crop fields and forests. The diversity of research studies necessitates a literature review for more progress and contribution in the agricultural field. In this review, the comparative analysis of existing literature surveys is explored. This paper aims to provide an overview of AIbased UAV swarms, different cameras and sensors, image processing, and machine learning (ML) algorithms for image analysis having the purpose of monitoring and disease identification. Brassica plants are focused as they are grown on wider scales globally. Brassica species, the commonly infected diseases, and different types of disease detection methods are discussed. Investigations show the significance of using UAV swarms for growth monitoring growth for yield estimation, health monitoring, water status monitoring and irrigation management, nutrition disorders monitoring, pest and disease detection, and pesticide and fertilizer spraying in Brassica plants. Finally, some challenges of swarm-based applications are also addressed that require future consideration. The significance of this paper is that it suggests its readers embrace swarm-based technologies in the pursuit of more efficient production with relevant economic benefits.
This comprehensive review highlights the significant strides made in the field of food freshness detection through the integration of deep learning and imaging techniques. By leveraging advanced neural networks, researchers have developed innovative methodologies that enhance the accuracy and efficiency of freshness monitoring. The fusion of various imaging modalities, with sophisticated deep learning algorithms has enabled more precise detection of quality attributes and spoilage indicators. This multidimensional approach not only improves the reliability of freshness assessments but also provides a more holistic view of condition of the food. Additionally, the review underscores the growing potential for these technologies to be applied in real-time monitoring systems, offering valuable insights for both producers and consumers. The advancements discussed pave the way for future research and development, emphasizing the need for continued innovation in integrating these technologies to address the challenges of food safety and quality assurance in an increasingly complex and dynamic market.
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