The automatic detection, visualization, and classification of plant diseases through image datasets are key challenges for precision and smart farming. The technological solutions proposed so far highlight the supremacy of the Internet of Things in data collection, storage, and communication, and deep learning models in automatic feature extraction and feature selection. Therefore, the integration of these technologies is emerging as a key tool for the monitoring, data capturing, prediction, detection, visualization, and classification of plant diseases from crop images. This manuscript presents a rigorous review of the Internet of Things and deep learning models employed for plant disease monitoring and classification. The review encompasses the unique strengths and limitations of different architectures. It highlights the research gaps identified from the related works proposed in the literature. It also presents a comparison of the performance of different deep learning models on publicly available datasets. The comparison gives insights into the selection of the optimum deep learning models according to the size of the dataset, expected response time, and resources available for computation and storage. This review is important in terms of developing optimized and hybrid models for plant disease classification.

The tumors on the skin are characterized by a high mortality rate. Research is attempting the automatic early diagnosis of melanoma, a lethal form of skin cancer, from dermoscopic images. Automatic diagnostics provides a valid “second opinion“ to support physicians in deciding whether a skin lesion is a benign mole or a malignant melanoma. Determining effective detection methods to reduce the rate of error in diagnosis is a crucial challenge. Computer vision systems are characterized by several fundamental steps. Preprocessing is the first phase of detection and plays a fundamental role: the elimination of noise and irrelevant parts against the background of skin images to improve image quality. The purpose of this paper is to review the pre-processing approaches that can be used on skin cancer images. The current interest in the automatic analysis of images, is motivated by the possibility of being able to provide quantitative information on a lesion and to implement self diagnosis solutions.

Following the general trend, the amount of digital information in the stored electronic health records (EHRs) had an explosion in the last decade. EHRs are not anymore used, as in the past, to store basic information of the patient and administrative tasks, but they may include a range of data, including the medical history of the patient, laboratory test results, demographics, medication and allergies, immunization status, radiology images, vital signs. At the present, the problem has shifted from collecting massive amounts of data to understanding it, i.e. use EHRs for turning data into knowledge, conclusions and actions. EHRs were not designed to forecast disease risk or disease progression or to determine the right treatment, but if they are combined with artificial intelligence (AI) algorithm this issue became possible. The need for tools allowing to construct predictive models capturing disease progression is a priority. In the recent past EHRs were analyzed using traditional machine learning techniques, whereas recently the progress in the field of deep learning let to the application of deep learning techniques to EHRs. This paper reports a brief overview of some recently developed deep learning tools for EHRs.