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Diagram showing the proposed validation and deployment of machine learning models developed for in-field (vineyard) detection of smoke contamination in berries using NIR, and in canopies using unmanned aerial vehicle (UAV) with an e-nose and thermal infrared camera as
payload.

Diagram showing the proposed validation and deployment of machine learning models developed for in-field (vineyard) detection of smoke contamination in berries using NIR, and in canopies using unmanned aerial vehicle (UAV) with an e-nose and thermal infrared camera as payload.

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Article
While starting a new 2021-22 grape-growing season in Australia, potential climatic anomalies such as bushfires are in the consciousness of many grape growers and winemakers. Increasing ambient temperatures has resulted in associated climatic anomalies, such as extreme wildfires in Australia, California, Siberia, Greece and Turkey as forecasted and...

Citations

... Machine learning is a reliable and promising modeling tool that has been used for the prediction and automation of tasks in many industries, including medical [45], oil and gas [46], manufacturing [47], automotive [48], agriculture [49], and food [50]. Some specific applications of machine learning in the agriculture and food industries are on grapes [51], wine [52,53], beer [54,55], honey [56], and coffee [25], among others. According to previous studies, the application of sensors technology combined with machine learning has demonstrated that this technique efficiently produces high accuracy and robust prediction models [26,[57][58][59]. ...
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Article
Aroma and other physicochemical parameters are important attributes influencing consumer perception and acceptance of rice. However, current methods using multiple instruments and laboratory analysis make these assessments costly and time-consuming. Therefore, this study aimed to assess rice quality traits of 17 commercial rice using a low-cost electronic nose and portable near-infrared spectrometer coupled with machine learning (ML). Specifically, artificial neural networks (ANN) were used to classify the type of rice and predict rice quality traits (aromas, color, texture and pH of cooked rice) as targets. The ML models developed showed that the chemometrics obtained from both sensor technologies successfully classified the rice (Model 1: 98.7%; Model 2: 98.6%) and predicted the peak area of aromas obtained by gas chromatography-mass spectroscopy found in raw (Model 3: R = 0.95; Model 6: R = 0.95) and cooked rice (Model 4: R = 0.98; Model 7: R = 0.96). Furthermore, a high R=0.98 was obtained for Model 5 to estimate the color, texture, and pH of cooked rice. The proposed method is rapid, low-cost, reliable and may help the rice industry increase high-quality rice production and accelerate the adoption of digital technologies and artificial intelligence to support the rice value chain.
... The latter models presented accuracies higher than 90% for classification and with correlation coefficients of R > 0.95 for regression models to predict specific smoke-related compounds in berries and wines. Implementing these digital technologies was the first attempt to transform the grape and wine industry production from traditionally reactive to a more predictive process using smart decision making [13,14]. For fault detection in fermented beverages, previous research has shown high accuracy for artificial intelligence (AI) tools using NIR, e-noses, and machine learning for beer [15]. ...
Full-text available
Article
The winemaking industry can benefit greatly by implementing digital technologies to avoid guesswork and the development of off-flavors and aromas in the final wines. This research presents results on the implementation of near-infrared spectroscopy (NIR) and a low-cost electronic nose (e-nose) coupled with machine learning to detect and assess wine faults. For this purpose, red and white base wines were used, and treatments consisted of spiked samples with 12 faults that are traditionally formed in wines. Results showed high accuracy in the classification models using NIR and e-nose for red wines (94%–96%; 92%–97%, respectively) and white wines (96%– 97%; 90%–97%, respectively). Implementing new and emerging digital technologies could be a turning point for the winemaking industry to become more predictive in terms of decision-making and maintaining and increasing wine quality traits in a changing and challenging climate.