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The main purpose of the current study was to propose innovative composite films based on a corn starch/polyvinyl alcohol (PVA) blend (starch:PVA 40:60) and loaded with 3 different levels of chitosan nanoparticles (CNPs) (1, 3, and 5% w/v) to strengthen its physical, mechanical, structural, thermal and antimicrobial attributes. The synthesized CNPs were spherical with a particle size of ca. 100 nm as demonstrated by scanning electron microscopy (SEM) micrographs and dynamic light scattering tests. The results showed that the CNPs incorporation within the starch-PVA 40:60 films promoted a uniform surface without any considerable pores. These films were characterized by a homogeneous CNP distribution within the polymer matrix, causing a significant decrease in water vapor permeability (WVP) (e.g., from 0.41 for the control film to 0.28 g·mm/kPa·h·m2 for the composite film loaded with 5% CNPs). The film solubility, transparency, glass transition and melting temperatures, and elongation at break were also reduced by increasing the CNP content from 1% to 5%, while total color and tensile strength parameters increased. The antibacterial effects of CNPs were more effective against Gram-positive bacteria (Staphylococcus aureus) than Gram-negative bacteria (Escherichia coli and Salmonella typhimurium). It can be concluded that the addition of CNPs to the starch-PVA matrix could improve its functional and technological attributes for food packaging applications.

Extending over millennia, grapevine cultivation encompasses several thousand cultivars. Cultivar (cultivated variety) identification is traditionally dealt by ampelography, requiring repeated observations by experts along the growth cycle of fruiting plants. For on-time evaluations, molecular genetics have been successfully performed, though in many instances, they are limited by the lack of referable data or the cost element. This paper presents a convolutional neural network (CNN) framework for automatic identification of grapevine cultivar by using leaf images in the visible spectrum (400–700 nm). The VGG16 architecture was modified by a global average pooling layer, dense layers, a batch normalization layer, and a dropout layer. Distinguishing the intricate visual features of diverse grapevine varieties, and recognizing them according to these features was conceivable by the obtained model. A five-fold cross-validation was performed to evaluate the uncertainty and predictive efficiency of the CNN model. The modified deep learning model was able to recognize different grapevine varieties with an average classification accuracy of over 99%. The obtained model offers a rapid, low-cost and high-throughput grapevine cultivar identification. The ambition of the obtained tool is not to substitute but complement ampelography and quantitative genetics, and in this way, assist cultivar identification services.

On-time seed variety recognition is critical to limit qualitative and quantitative yield loss and asynchronous crop production. The conventional method is a subjective and error-prone process, since it relies on human experts and usually requires accredited seed material. This paper presents a convolutional neural network (CNN) framework for automatic identification of chickpea varieties by using seed images in the visible spectrum (400–700 nm). Two low-cost devices were employed for image acquisition. Lighting and imaging (background, focus, angle, and camera-to-sample distance) conditions were variable. The VGG16 architecture was modified by a global average pooling layer, dense layers, a batch normalization layer, and a dropout layer. Distinguishing the intricate visual features of the diverse chickpea varieties and recognizing them according to these features was conceivable by the obtained model. A five-fold cross-validation was performed to evaluate the uncertainty and predictive efficiency of the CNN model. The modified deep learning model was able to recognize different chickpea seed varieties with an average classification accuracy of over 94%. In addition, the proposed vision-based model was very robust in seed variety identification, and independent of image acquisition device, light environment, and imaging settings. This opens the avenue for the extension into novel applications using mobile phones to acquire and process information in situ. The proposed procedure derives possibilities for deployment in the seed industry and mobile applications for fast and robust automated seed identification practices.