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In this paper we investigate the problem of Grocery product recognition using iconic images. Iconic images are used to advertise products and they are very different from images that are captured instore. We investigate the use of learned features for the retrieval process. We evaluated different feature extraction strategies using Convolutional Neural Networks (CNNs) and tested the CNNs on the Grocery Store image dataset that contains 81 product categories grouped into 43 coarsegrained classes and 3 macro classes. Results show that a Siamese network with a DenseNet-169 backbone better captures relations between iconic and in-store images outperforming other architectures in the retrieval task.

Precision agriculture has emerged as a promising approach to improve crop productivity and reduce the environmental impact. However, effective decision making in precision agriculture relies on accurate and timely data acquisition, management, and analysis. The collection of multisource and heterogeneous data for soil characteristics estimation is a critical component of precision agriculture, as it provides insights into key factors, such as soil nutrient levels, moisture content, and texture. To address these challenges, this work proposes a software platform that facilitates the collection, visualization, management, and analysis of soil data. The platform is designed to handle data from various sources, including proximity, airborne, and spaceborne data, to enable precision agriculture. The proposed software allows for the integration of new data, including data that can be collected directly on-board the acquisition device, and it also allows for the incorporation of custom predictive systems for soil digital mapping. The usability experiments conducted on the proposed software platform demonstrate that it is easy to use and effective. Overall, this work highlights the importance of decision support systems in the field of precision agriculture and the potential benefits of using such systems for soil data management and analysis.