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Malaria is a significant health concern worldwide, particularly in Africa where its prevalence is still alarmingly high. Using artificial intelligence algorithms to diagnose cells with malaria provides great convenience for clinicians. In this paper, a densely connected convolutional dynamic learning network (DCDLN) is proposed for the diagnosis of malaria disease. Specifically, after data processing and partitioning of the dataset, the densely connected block is trained as a feature extractor. To classify the features extracted by the feature extractor, a classifier based on a dynamic learning network is proposed in this paper. Based on experimental results, the proposed DCDLN method demonstrates a diagnostic accuracy rate of 97.23%, surpassing the diagnostic performance than existing advanced methods on an open malaria cell dataset. This accurate diagnostic effect provides convincing evidence for clinicians to make a correct diagnosis. In addition, to validate the superiority and generalization capability of the DCDLN algorithm, we also applied the algorithm to the skin cancer and garbage classification datasets. DCDLN achieved good results on these datasets as well, demonstrating that the DCDLN algorithm possesses superiority and strong generalization performance. [ABSTRACT FROM AUTHOR]

• Agriculture productivity in Africa and most developing countries are far from optimum. • Digitalization is a potential game changer for agricultural productivity. • Sensors, IoT, AI, and smartphones are key for Africa's agricultural digitalization. • Digitalization could strengthen African food security and globally. • Infrastructure and policy challenges digitalization on the continent. Africa's agriculture has over time, improved and expanded in scale and efficiency, yet its productivity is far from its optimum, and the majority of the continent is still food insecure. To harness its potential for a drastic increase to avoid continued mass hunger and support the bioeconomy's growth, farm productivity in Africa must be retooled. This paper examines the role of digitalization as a potential game changer and an impetus for a transformational future for large-scale and smallholder farming and livelihoods through driving technological and methodological innovation and automation. Digitalization is seen here as consisting of four supporting elements: sensors (in situ or remote), wireless transmission of data, artificial intelligence (AI), and transmission of diagnoses and or recommendations to the end user. In addition to strengthening food security on the continent and globally and the ensuing economic benefits, other cross-cutting effects are presented. These include risk management through early warning systems for natural disasters, improved decision-making with big data, reduction in rural-urban migration by the attraction of youth towards agriculture, and the improvement in environmental stewardship and building climate resilience. Challenges to realizing the benefits of digitalization, including lack of infrastructure and human resource expertise and the role of public-private partnerships in developing solutions, are also addressed. [ABSTRACT FROM AUTHOR]