Crop diseases have mainly affected crop production due to the lack of modern approaches for disease identification. For many years, farmers have identified various crop diseases and have local knowledge about disease management. However, the local knowledge of one agricultural region is not utilized in other regions due to the unavailability of knowledge sharing platforms. Agricultural research also suggests that crop production has mainly decreased due to diseases, methods of cultivation, irrigation, and lack of local agricultural knowledge. In this research, the experience of agricultural experts, farmers, and cultivators is gathered through a crowd-sourced platform. The data is then processed for various disease identification. Hence, timely identification of various crop diseases can benefit farmers to apply relevant management methods. In literature, researchers have proposed various methods for disease management, mostly based on the classification of crop diseases using Machine Learning (ML) algorithms. However, these algorithms are unable to give trustful results due to static data provisioning and the dynamic nature of various diseases in different agricultural regions. Further, the agricultural expert's experience is also not considered in verifying the classification results. To identify the dynamic nature of wheat diseases, we acquired high-quality images and symptoms-based text data from farmers, domain experts, and users using a crowd-sourced platform. Different augmentation techniques were also used to enhance the size of training data. In this paper, a modern generic approach has been proposed for the identification and classification of wheat diseases using Decision Trees (DT) and different deep learning models. Also, results of both algorithms were then verified by domain experts that improved the decision trees accuracy by 28.5%, CNN accuracy by 4.3% (leading to 97.2%), and resulted in decision rules for wheat diseases in a knowledge-based system.

Internet of Things (IoT) is a global network of physical and virtual ‘things’ connected to the internet. Each object has unique ID which is used for identification. IoT is the emerging technology which will change the way we interact with devices. In future almost every electronic device will be a smart device which can compute and communicate with hand-held and other infrastructure devices. As most of the devices may be battery operated, due to less processing power the security and privacy is a major issue in IoT. Authentication, Identification and device heterogeneity are the major security and privacy concerns in IoT. Major challenges include integration, scalability, ethics communication mechanism, business models and surveillance. In this paper major issues related to security and privacy of IoT are focused.

Monitoring and controlling the substations is a significant task in this period of automation to provide consumers with safe electricity. But the danger of blackouts, brownouts, and fires is rapidly increasing due to the old configuration of distribution grids (substations) and the absence of automation systems to monitor the vital conditions in substations systems. The substation includes various electronic components such as transformers, breakers, and relays. Overheating can lead to transformer fluid leakage or internal insulation breakdown problems. The old technique requires manual system checking that occurs periodically and with very little precision. Moreover, substations in urban areas are more difficult to check physically and thus take additional time to carry out related activities. We proposed a system that is low cost, user friendly, and works in auto mode to avoid labor involvement, electricity loss. The results of the system are displayed in many ways to assure that the system parameters will be monitored and controlled by more than one person because of safety and protection reasons. The uniqueness of this system is the display of results on desktop and mobile phones simultaneously. The result on the CAYENNE platform validates the performance of our proposed system in real-time monitoring, data logging, and controlling.

This research study focuses on developing and implementing an IoT-based distribution transformer monitoring and protection system. The traditional methods of transformer protection and monitoring have proven to be inefficient and time-consuming, leading to the need for a more modern and effective solution. In this study, a low-cost prototype system is proposed to handle and control the main functions and problems of the distribution transformer through the internet. The proposed system allows for easy monitoring and protection of the transformer, enabling electric companies to improve efficiency and reduce labour and tool costs. The system is validated using Proteus software to simulate and obtain results from the hardware. The system results are displayed in multiple ways, including LCD, system bar, and internet, making it easier for electric companies and consumers in developing countries like Pakistan to monitor and control distribution transformers efficiently. This research study aims to provide valuable insights into the effectiveness of IoT-based distribution transformer monitoring and protection systems and their potential benefits in enhancing transformer performance and efficiency.