Safflower is one of the oldest cultivated crops, usually grown at a small scale. Safflower is grown for flowers used for coloring, flavoring foods, dyes, medicinal properties, and livestock feed. Safflower is underutilized but gaining attention due to oil yield potential and the ability to grow under high temperatures, drought, and salinity. Salinity and drought have negative effects by disrupting the ionic and osmotic equilibrium of the plant cells. The stress signal is perceived by membranes then transduced in the cell to switch on the stress responsive genes. This review discusses on stress tolerance mechanisms in safflower. Strategies are proposed for enhancing drought and salt resistance in safflower.

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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.

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