Artificial Intelligence : Implications for the Agri-Food Sector

Artificial intelligence (AI) involves the development of algorithms and computational models that enable machines to process and analyze large amounts of data, identify patterns and relationships, and make predictions or decisions based on that analysis. AI has become increasingly pervasive across a wide range of industries and sectors, with healthcare, finance, transportation, manufacturing, retail, education, and agriculture are a few examples to mention. As AI technology continues to advance, it is expected to have an even greater impact on industries in the future. For instance, AI is being increasingly used in the agri-food sector to improve productivity, efficiency, and sustainability. It has the potential to revolutionize the agri-food sector in several ways, including but not limited to precision agriculture, crop monitoring, predictive analytics, supply chain optimization, food processing, quality control, personalized nutrition, and food safety. This review emphasizes how recent developments in AI technology have transformed the agri-food sector by improving efficiency, reducing waste, and enhancing food safety and quality, providing particular examples. Furthermore, the challenges, limitations, and future prospects of AI in the field of food and agriculture are summarized

Near-Infrared Spectroscopy and Machine Learning: Analysis and Classification Methods of Rice

Nowadays, the conventional biochemical methods used to differentiate and characterize rice types, biochemical properties, authentication, and contamination issues are difficult to implement due to the high cost of reagents, time requirement and environmental issues. Actually, the success of agri-food technology is directly related to the quality of analysis of experimental data acquired by sensors or techniques such as the infrared-spectroscopy. To overcome these technical limitations, a rapid and non-destructive methodology for discrimination and classification of rice has been investigated. Near-infrared spectroscopy is considered as fast, clean, and non-destructive analytical tools and its spectra present significant biomolecular information that must be analysed by sophisticated methodologies. Machine learning plays an important role in the analysis of the spectral data being used several methods such as Partial Least Squares, Principal Component Analysis, Partial Least Squares-Discriminant Analysis, Support Vector Machine, Artificial Neuronal Network, among others which can successfully be applied for food classification and discrimination as well as in terms of authentication and contamination issues. The quality control of rice is extremely important at every stage of production, beginning with estimation of raw agricultural materials and monitoring their quality during storage, estimating food quality during the production process and of the final products as well as the determination of their authenticity and the detection of adulterants

Application of Color and Size Measurement in Food Products Inspection

Color and size are external aspects considered by consumers in purchasing a food product and are used in food product inspection using computer vision. This paper reviews recent applications of color and size measurement in food product inspection using computer vision. RGB, HSI, HSL, HSV, La*b spaces and color index are widely used to measure color in food product inspection. Color features, including value, mean, variance, and standard deviation of each channel in a color space are widely used in food product inspection. The applications of color measurement in food product inspection are for grading, detection of anomaly or damage, detection of specific content and evaluation of color changes. Length, width, thickness, average radius, Feret’s diameter, area, perimeter, volume, and surface area are common size measurements in food product inspection. The applications of size measurement in food product inspection are for estimating size, sorting, grading, detect unwanted objects or defects, and measurement of physical properties

Spectral study of heat treatment process of wheat flour by VIS/SW-NIR image system

The capability of the VIS/SW-NIR (visible/short wave near infrared) hyperspectral imaging system to characterize the heat treatment process of cake wheat flour was studied. Combinations of heat treatments of flour were run at different temperatures (80, 100 and 130 °C) and for various times (10, 20 and 30 min). The resulting treated flours were analyzed by the imaging technique. The hyperspectral results, studied by multivariate statistical methods, showed a pattern evolution of the flours treated by different heat treatments. The wavelengths that contributed the most, and implied in the differentiations, were detected. The selection of wavelengths allowed us to optimize the analysis, which reduced from 54 to 6 wavelengths. To ensure that the VIS/SW-NIR information depended on the heat treatment influence on flours, cakes were produced and characterized according to height, mass loss during the baking process, crumb structure and textural properties. The VIS/SW-NIR imaging analysis was capable of following the changes that occurred during the different heat treatments of flours. VIS/SW-NIR was applied to determine and adjust the heat treatment process variables to improve the features of flours during the cake production process.Verdú Amat, S.; Ivorra Martínez, E.; Sánchez Salmerón, AJ.; Barat Baviera, JM.; Grau Meló, R. (2016). Spectral study of heat treatment process of wheat flour by VIS/SW-NIR image system. Journal of Cereal Science. 71:99-107. doi:10.1016/j.jcs.2016.08.008S991077

Genetic improvement of grain yield and quality in rice (Oryza sativa L.) in South Sudan.

Doctor of Philosophy in Plant Breeding. University of KwaZulu-Natal, Pietermaritzburg, 2017.Abstract available in PDF file