This paper presents the experimental evaluation and performance analysis of CropCare+, a drone-assisted precision agriculture system designed for real-time crop monitoring and automated intervention. The proposed system integrates aerial crop health assessment, targeted pesticide and water spraying, soil moisture sensing, and automated irrigation control into a unified platform. Building upon the system architecture introduced in the previous work, this study focuses on the machine learning dataset, model training, hardware implementation, and field-level performance validation. A lightweight convolutional neural network model was trained on a labeled crop image dataset to classify plant conditions such as healthy growth, pest infestation, disease symptoms, and water stress. The trained model was deployed on a Raspberry Pi-based onboard unit using edge inference, enabling real-time decision-making during drone operation. Simultaneously, an ESP32-based ground unit monitored soil moisture and environmental parameters to control irrigation through a threshold-based mechanism. Experimental tests were conducted in a controlled field environment to evaluate classification accuracy, resource utilization, and system responsiveness. The results demonstrate improved detection accuracy, reduced water and pesticide usage, and faster response times compared to conventional manual methods. The integrated approach significantly reduces labor requirements while enabling data-driven, localized interventions. The findings confirm the practical feasibility of the CropCare+ system as a scalable, cost-effective solution for intelligent and sustainable precision agriculture.

This paper presented the design and implementation of CropCare+, an integrated precision agriculture system that combines drone technology, machine learning–based crop disease detection, and IoT-enabled environmental monitoring to improve agricultural efficiency. The proposed system utilizes a drone platform equipped with a camera and relay-controlled spraying mechanism to monitor crop health and perform targeted pesticide and water spraying. In addition to aerial monitoring, a ground-based sensing system was implemented using multiple environmental sensors to continuously measure parameters such as soil moisture, temperature, and nutrient levels. The captured leaf images are processed using a trained machine learning model capable of classifying plant health conditions and identifying diseased crops. When a disease is detected, the system automatically activates the pesticide spraying mechanism to treat the affected plants. Simultaneously, the ground-based sensor system monitors soil conditions and triggers irrigation when the soil moisture level falls below a predefined threshold. Communication between the ground monitoring unit and the processing system is achieved using LoRa-based wireless transmission, enabling reliable long-range data communication suitable for agricultural environments. Experimental evaluation demonstrated that the system can effectively identify crop diseases, optimize water and pesticide usage, and automate irrigation based on real-time environmental data. The integration of drone-based monitoring with IoT sensing provides a comprehensive solution for precision agriculture, enabling early detection of plant stress conditions and reducing unnecessary resource consumption. Overall, the proposed system highlights the potential of combining artificial intelligence, drone technology, and wireless sensor networks to support modern smart farming practices. By enabling automated crop monitoring and targeted agricultural interventions, the CropCare+ system contributes toward improving crop productivity, reducing labor requirements, and promoting sustainable agricultural resource management.

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