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Accurate and rapid plant disease detection is critical for enhancing long-term agricultural yield. Disease infection poses the most significant challenge in crop production, potentially leading to economic losses. Viruses, fungi, bacteria, and other infectious organisms can affect numerous plant parts, including roots, stems, and leaves. Traditional techniques for plant disease detection are time-consuming, require expertise, and are resource-intensive. Therefore, automated leaf disease diagnosis using artificial intelligence (AI) with Internet of Things (IoT) sensors methodologies are considered for the analysis and detection. This research examines four crop diseases: tomato, chilli, potato, and cucumber. It also highlights the most prevalent diseases and infections in these four types of vegetables, along with their symptoms. This review provides detailed predetermined steps to predict plant diseases using AI. Predetermined steps include image acquisition, preprocessing, segmentation, feature selection, and classification. Machine learning (ML) and deep understanding (DL) detection models are discussed. A comprehensive examination of various existing ML and DL-based studies to detect the disease of the following four crops is discussed, including the datasets used to evaluate these studies. We also provided the list of plant disease detection datasets. Finally, different ML and DL application problems are identified and discussed, along with future research prospects, by combining AI with IoT platforms like smart drones for field-based disease detection and monitoring. This work will help other practitioners in surveying different plant disease detection strategies and the limits of present systems.

This research aimed to predict soil’s physical and chemical properties with a state-of-the-art hybrid model based on deep learning algorithms and optical satellite images in a region in the north of Iran. As dependent data, 317 soil samples (0–30 cm) were collected in field surveying and analyzed by the soil and water research institute for their physical (clay, silt, and sand) and chemical [electrical conductivity (EC), organic carbon (OC), phosphorus (P), soil reaction (pH), and potassium (K)] properties. Based on independent data, 23 remote sensing (RS) parameters (extracted from Landsat 8 optical images), 17 topographical parameters [extracted from the digital elevation model (DEM)], and four climatic parameters (derived from the meteorological organization). Spatial prediction of physical and chemical properties was implemented using a convolutional neural network (CNN), recurrent neural network (RNN), and hybrid CNN-RNN models. The evaluation results indicated that the hybrid CNN-RNN model had higher accuracy in all soil properties, followed by the RNN and CNN models. In the hybrid CNN-RNN model, pH (0.0206), EC (0.0958 dS/m), silt (0.0996%), P (0.1078 ppm), K (0.1185 ppm), sand (0.1360%), OC (0.1361%), and clay (0.1419%) had higher prediction accuracy, as determined by the root mean-squared error (RMSE) index. The hybrid CNN-RNN model proved to be the most effective for soil property prediction in this region. This finding underscores the potential of deep learning techniques in harnessing RS data for precise soil property mapping, with implications for land management and agricultural practices.

The emergence of several novel SARS-CoV-2 variants regarded as variants of concern (VOCs) has exacerbated pathogenic and immunologic prominences, as well as reduced diagnostic sensitivity due to phenotype modification-capable mutations. Furthermore, latent and more virulent strains that have arisen as a result of unique mutations with increased evolutionary potential represent a threat to vaccine effectiveness in terms of incoming and existing variants. As a result, resisting natural immunity, which leads to higher reinfection rates, and avoiding vaccination-induced immunization, which leads to a lack of vaccine effectiveness, has become a crucial problem for public health around the world. This study attempts to review the genomic variation and pandemic impact of emerging variations of concern based on clinical characteristics management and immunization effectiveness. The goal of this study is to gain a better understanding of the link between genome level polymorphism, clinical symptom manifestation, and current vaccination in the instance of VOCs.

Weeds are among the significant factors that could harm crop yield by invading crops and smother pastures, and significantly decrease the quality of the harvested crops. Herbicides are widely used in agriculture to control weeds; however, excessive use of herbicides in agriculture can lead to environmental pollution as well as yield reduction. Accurate mapping of crops/weeds is essential to determine weeds’ location and locally treat those areas. Increasing demand for flexible, accurate and lower cost precision agriculture technology has resulted in advancements in UAS-based remote sensing data collection and methods. Deep learning methods have been successfully employed for UAS data processing and mapping tasks in different domains. This research investigate, compares and evaluates the performance of deep learning methods for crop/weed discrimination on two open-source and published benchmark datasets captured by different UASs (field robot and UAV) and labeled by experts. We specifically investigate the following architectures: 1) U-Net Model 2) SegNet 3) FCN (FCN-32s, FCN-16s, FCN-8s) 4) DepLabV3+. The deep learning models were fine-tuned to classify the UAS datasets into three classes (background, crops, and weeds). The classification accuracy achieved by U-Net is 77.9% higher than 62.6% of SegNet, 68.4% of FCN-32s, 77.2% of FCN-16s, and slightly lower than 81.1% of FCN-8s, and 84.3% of DepLab v3+. Experimental results showed that the ResNet-18 based segmentation model such as DepLab v3+ could precisely extract weeds compared to other classifiers.

The evaluation and confirmation of healing properties of several plant species of genus Terminalia based on their traditional uses and the clinical claims are of utmost importance. Genus Terminalia has received more attention to assess and validate the therapeutic potential and clinical approval due to its immense folk medicinal and traditional applications. Various species of Terminalia genus are used in the form of herbal medicine and formulations, in treatment of diseases, including headache, fever, pneumonia, flu, geriatric, cancer, to improve memory, abdominal and back pain, cough and cold, conjunctivitis, diarrhea, heart disorder, leprosy, sexually transmitted diseases, and urinary tract disorders. These are reported to possess numerous biological properties, counting: antibacterial, antifungal, antiinflammatory, antiviral, antiretroviral, antioxidant, and antipa7rasitic. This current research review aims to update the detailed biological activities, pre-clinical and clinical studies of various extracts and secondary metabolites from several plant species under the genus Terminalia, along with information on the traditional uses and chemical composition to develop a promising strategy for their potential applications in the form of medicine or use in modern drug formulations for treating diseases like pneumonia, flu, and other types of viral infections or controlling human contagions.