Assessment of Plant Germination Intensity with the Use of Automated System with Computer Vision Method

Abstract

The objective of the paper was to show various options of using by author an automated stand with computer image analysis for control of plant germination on the example of cauliflower Brassica oleracea L. 'Pionier" variety. The developed system consisted of a mobile platform equipped with the acquisition and image processing system based on Raspberry PL processor. Germination of cauliflower seeds was the object of observation, which in one case were sown to soil after dressing them with plant extracts (sweet flag Acorus calamus L., great burdock roots Arctium lappa L.). In the other case, undressed seeds were sown in the place of previous application of the abovementioned extracts. The use of a robot for monitoring plant germination enabled the automated analysis of the investigated material with higher frequency than it has been possible so far. Simultaneously, higher germination was reported when seeds were treated with macerates and extracts from great burdock roots.

Brand Marketing Decision Support System Based on Computer Vision and Parallel Computing

“…Target recognition methods include traditional target recognition methods and target recognition methods based on deep learning. In the traditional target recognition method, the original image is preprocessed first, then the image features are extracted manually, feature selection is performed, and finally, the design and training of the classifier are performed [6]. Traditional target detection has the following two main problems: first, the region selection strategy based on sliding window is not targeted, time complexity is high, and the window is redundant; second, the handdesigned features are not very good for diversity changes of robustness.…”