Deep learning in agriculture: A survey

Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.info:eu-repo/semantics/acceptedVersio

Deep learning in agriculture: A survey

Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques

The rise of blockchain technology in agriculture and food supply chains

Blockchain is an emerging digital technology allowing ubiquitous financial transactions among distributed untrusted parties, without the need of intermediaries such as banks. This article examines the impact of blockchain technology in agriculture and food supply chain, presents existing ongoing projects and initiatives, and discusses overall implications, challenges and potential, with a critical view over the maturity of these projects. Our findings indicate that blockchain is a promising technology towards a transparent supply chain of food, with many ongoing initiatives in various food products and food-related issues, but many barriers and challenges still exist, which hinder its wider popularity among farmers and systems. These challenges involve technical aspects, education, policies and regulatory frameworks.info:eu-repo/semantics/acceptedVersio

Selective factors involved in oil flotation isolation of black yeasts from the environment

The oil flotation isolation technique has been successfully applied to recover chaetothyrialean black yeasts and relatives from the environment. The selective mechanisms playing a role in isolation are unknown. The fungi concerned are supposed to occupy specialized microniches in nature, taking advantage of (1) oligotrophism. Mineral oil as a main selective agent may be based on (2) hydrophobicity or on (3) assimilation. All three hypotheses are tested in this paper. Results show that cell wall hydrophobicity is unlikely to be a selective factor. Incubation under poor nutrient conditions provides competitive advantage for black yeasts, especially for Exophiala strains, which are subsequently enriched by mineral oil which enhances growth in this group of fungi. Incubation under mineral media and mineral oil can be used as selective factor