This paper focus on multiple CNN-based (Convolutional Neural Network) models for COVID-19 forecast developed by our research team during the first French lockdown. In an effort to understand and predict both the epidemic evolution and the impacts of this disease, we conceived models for multiple indicators: daily or cumulative confirmed cases, hospitalizations, hospitalizations with artificial ventilation, recoveries, and deaths. In spite of the limited data available when the lockdown was declared, we achieved good short-term performances at the national level with a classical CNN for hospitalizations, leading to its integration into a hospitalizations surveillance tool after the lockdown ended. Also, A Temporal Convolutional Network with quantile regression successfully predicted multiple COVID-19 indicators at the national level by using data available at different scales (worldwide, national, regional). The accuracy of the regional predictions was improved by using a hierarchical pre-training scheme, and an efficient parallel implementation allows for quick training of multiple regional models. The resulting set of models represent a powerful tool for short-term COVID-19 forecasting at different geographical scales, complementing the toolboxes used by health organizations in France.

During the last decades, researchers have developed novel computing methods to help viticulturists solve their problems, primarily those linked to yield estimation of their crops. This article aims to summarize the existing research associated with computer vision and viticulture. It focuses on approaches that use RGB images directly obtained from parcels, ranging from classic image analysis methods to Machine Learning, including novel Deep Learning techniques. We intend to produce a complete analysis accessible to everyone, including non-specialized readers, to discuss the recent progress of artificial intelligence (AI) in viticulture. To this purpose, we present work focusing on detecting grapevine flowers, grapes, and berries in the first sections of this article. In the last sections, we present different methods for yield estimation and the problems that arise with this task.

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The development of Artificial Intelligence has raised interesting opportunities for improved automation in smart agriculture. Smart viticulture is one of the domains that can benefit from Computer-vision tasks through field sustainability. Computer-vision solutions present additional constraints as the amount of data for good training convergence has to be complex enough to cover sufficient features from desired inputs. In this paper, we present a study to implement a grapevine detection improvement for early grapes detection and grape yield prediction whose interest in Champagne and wine companies is undeniable. Earlier yield predictions allow a better market assessment, the harvest work’s organization and help decision-making about plant management. Our goal is to carry estimations 5 to 6 weeks before the harvest. Furthermore, the grapevines growing condition and the large amount of data to process for yield estimation require an embedded device to acquire and compute deep learning inference. Thus, the grapes detection model has to be lightweight enough to run on an embedded device. These models were subsequently pre-trained on two different types of datasets and several layer depth of deep learning models to propose a pseudo-labelling Teacher-Student related Knowledge Distillation. Overall solutions proposed an improvement of 7.56%, 6.98, 8.279%, 7.934% and 13.63% for f1 score, precision, recall, mean average precision at 50 and mean average precision 50-95 respectively on BBCH77 phenological stage.

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