This paper presents an intelligent integrated system for fruit detection in orchards using multi-UAV imaging and deep learning. The system aims to enhance the efficiency of fruit detection and counting, which is crucial for efficient orchard management and harvest preparation. Traditional techniques often fail to provide timely and precise data, making the integration of innovative solutions essential. The proposed approach combines artificial intelligence (AI), deep learning (DL), and unmanned aerial vehicles (UAVs) to achieve superior real-time capabilities in fruit detection and counting. The core innovation of the system is its ability to simultaneously capture and synchronize video frames from multiple UAV cameras, converting them into a cohesive data structure and a continuous image. This integration is further enhanced by image quality optimization techniques, ensuring high-resolution and accurate detection of targeted objects during UAV operations. The effectiveness of the system is demonstrated through experiments, achieving a high mean average precision rate of 86.8% in fruit detection and counting, which surpasses existing technologies. Additionally, it maintains low average error rates, with a false positive rate of 14.7% and a false negative rate of 18.3%, even under challenging weather conditions like cloudiness. The practical implications of this multi-UAV imaging and DL-based approach are vast, particularly for real-time fruit recognition in orchards, marking a significant stride forward in the realm of digital agriculture that aligns with the objectives of Industry 4.0. The system's effectiveness is validated through experiments conducted in various weather conditions, including sunny and cloudy periods, and under different lighting conditions. The results show that the system can accurately detect and count fruits, even in complex orchard environments, with high precision and recall rates. The system's performance is further evaluated using metrics such as mean average precision (mAP), false positive rate (FPR), and false negative rate (FNR), demonstrating its robustness and reliability. Overall, the proposed system represents a significant advancement in the field of precision agriculture, offering a comprehensive solution for real-time fruit detection and counting in orchards, enhancing the efficiency and accuracy of agricultural management and harvest preparation.This paper presents an intelligent integrated system for fruit detection in orchards using multi-UAV imaging and deep learning. The system aims to enhance the efficiency of fruit detection and counting, which is crucial for efficient orchard management and harvest preparation. Traditional techniques often fail to provide timely and precise data, making the integration of innovative solutions essential. The proposed approach combines artificial intelligence (AI), deep learning (DL), and unmanned aerial vehicles (UAVs) to achieve superior real-time capabilities in fruit detection and counting. The core innovation of the system is its ability to simultaneously capture and synchronize video frames from multiple UAV cameras, converting them into a cohesive data structure and a continuous image. This integration is further enhanced by image quality optimization techniques, ensuring high-resolution and accurate detection of targeted objects during UAV operations. The effectiveness of the system is demonstrated through experiments, achieving a high mean average precision rate of 86.8% in fruit detection and counting, which surpasses existing technologies. Additionally, it maintains low average error rates, with a false positive rate of 14.7% and a false negative rate of 18.3%, even under challenging weather conditions like cloudiness. The practical implications of this multi-UAV imaging and DL-based approach are vast, particularly for real-time fruit recognition in orchards, marking a significant stride forward in the realm of digital agriculture that aligns with the objectives of Industry 4.0. The system's effectiveness is validated through experiments conducted in various weather conditions, including sunny and cloudy periods, and under different lighting conditions. The results show that the system can accurately detect and count fruits, even in complex orchard environments, with high precision and recall rates. The system's performance is further evaluated using metrics such as mean average precision (mAP), false positive rate (FPR), and false negative rate (FNR), demonstrating its robustness and reliability. Overall, the proposed system represents a significant advancement in the field of precision agriculture, offering a comprehensive solution for real-time fruit detection and counting in orchards, enhancing the efficiency and accuracy of agricultural management and harvest preparation.