Plant diseases present substantial challenges to global agriculture, significantly affecting crop yields and jeopardizing food security. Accurate and timely detection of these diseases is paramount for mitigating their adverse effects. This paper proposes a novel approach for plant disease classification by integrating convolutional neural networks (CNNs) and graph convolutional networks (GCNs). The model aims to enhance classification accuracy by leveraging both visual features extracted by CNNs and relational information captured by GCNs. Using a Kaggle dataset containing images of diseased and healthy plant leaves from 31 classes, including apple, corn, grape, peach, pepper bell, potato, strawberry, and tomato. Standalone CNN models were trained on image data from each plant type, while standalone GCN models utilized graph-structured data representing plant relationships within each subset. The proposed integrated CNN-GCN model capitalizes on the complementary strengths of CNNs and GCNs to achieve improved classification performance. Through rigorous experimentation and comparative analysis, the effectiveness of the integrated CNN-GCN approach was evaluated alongside standalone CNN and GCN models across all plant types. Results demonstrated the superiority of the integrated model, highlighting its potential for enhancing plant disease classification accuracy.

For modern military operations regarding the safety and the situational awareness of soldiers in combat is paramount. In this paper we proposed an IoT based Soldier Status Monitoring System (SSMS) to address the Soldier Status issue to increase the battlefield awareness by provide real time soldier location tracking, health monitoring and alerts to the soldiers who are in danger. Multiple smart sensors like gas sensors, motion sensors, biometric sensors, metal detectors are integrated in helmet and boots of each soldier into the system. In the end, an IoT feeds soldiers’ locations back to a centralized command via GPS communications. In addition, an SOS switch enables soldiers to invoke an emergency channel. Using predictive analytics and machine learning, the system is able to identify early indications of danger or degradation to health that could mean increasing soldier safety. End to end encryption ensuresdata security and energy harvesting technology eases the burden of energy application on the computing device. This system represents a comprehensive solution to making soldiers safer, able to better communicate, and more operationally efficient on the modern battlefield.