Computer-advanced technologies have a significant impact across various fields. It is widely recognized that diseases have a detrimental effect on crop productivity and can significantly impact the economy, particularly in agricultural countries. Tomatoes hold great economic importance among cash crops, second only to potatoes. Globally, tomato production reaches a staggering 160 million tons annually, making it even more crucial for agricultural development. Unfortunately, the tomato crop is susceptible to several diseases, with early blight and late blight as two prominent culprits responsible for a production decrease of around 79%. Traditional disease detection and identification methods are time-consuming, expensive, and destructive, often requiring pathologists’ expertise. Thus, the primary research objective is to enhance disease identification accuracy by leveraging deep learning techniques. A model based on the inception-V3 architecture has been devised to classify diseases affecting tomato plant leaves. The model was trained and tested using the PlantVillage dataset, which comprises 6000 sample images of tomato leaves. The training and testing process utilized an 80 : 20 ratio, resulting in an impressive classification accuracy of 97.44% for the proposed model. The proposed solution aims to enable the tomato industry to thrive in the global market by mitigating the impact of tomato leaf diseases. By reducing the prevalence of these diseases, the solution can increase demand and contribute to the industry’s growth.

Internet of Vehicles (IoV) is turning out to be one of the first impressive examples of Internet of Things (IoT). In IoV, the factors of connectivity and interaction/information dispersion are equally important as sensing/actuating, context‐awareness, services provisioning, etc. However, most of the researches related to connectivity and interaction are constrained to physics of signaling and data science (semantics/contents), respectively. Very rapidly, the meanings of these factors are changing due to evolution of technologies from physical to social domain. For example, Social IoV (SIoV) is a term used to represent when vehicles build and manage their own social network. Hence, in addition to physical aspects, the social aspects of connectivity and information dispersion towards these systems of future should also be researched, a domain so far ignored in this particular context. In this paper, an agent‐based model of information sharing (for context‐based recommendations) of a hypothetical population of smart vehicles is presented. Some important hypotheses are tested under reasonable connectivity and data constraints. The simulation results reveal that closure of social ties and its timing impacts the dispersion of novel information (necessary for a recommender system) substantially. It is also observed that as the network evolves due to incremental interactions, the recommendations guaranteeing a fair distribution of vehicles across equally good competitors is not possible.

Many crowd evacuation models have been developed in isolation with respect to the aspects of interest. In this paper, we propose an agent-based model of crowd evacuation, pertaining to symmetric utilization of resources in terms of exit usage and evacuation time. The model is based on a game-theoretic decision support, resolving the consequences of proximity pressure on agents of two types; emotional and rational. This model is combined with the model of routing decision, in the presence of two possible information dispersion modalities; without and with inter-agent information sharing; to update agents' perception map. An agent-based simulation using a hypothetical space is performed to evaluate the performance of the model. The simulation results reveal that panic is minimal when agents adopt nearest exit strategy, a mechanized strategy with no human aspects and interactions. However, it takes more time and results in uneven distribution of agents across the exits. In spite of utilizing human aspects, the strategies involving information sharing between agents results in even distribution of agents across the exits with acceptable population panic.