The robotic harvesting platform's fruit and vegetable detection system is crucial. Due to uneven environmental factors such branch and leaf shifting sunshine, fruit and vegetable clusters, shadow, and so on, the fruit recognition has become more difficult in nowadays. The current method in this work is used to detect different types of fruits and vegetables in different size and shape. This method makes the use of OpenCV, Dark Flow, a TensorFlow variant of the YOLO technique. To train the necessary of network, a range of fruits and vegetable pictures were input into the network. The photos were pre-processed using OpenCV to create manual bounding boxes around the fruits and vegetables before into the training. YOLO detection algorithm is used. In, this method more accurately and rapidly recognizes of an item in an image. After the network has been trained, the test input is sent into the bounding boxes surrounding the recognized fruits and vegetables will be displayed as a consequence.

The next generation 6G era is considered to be highly coupled with intelligent network management and orchestration, while 5G is completely renowned for micro-service architecture-based network cloudification. 6G has been revolutionized for satisfying the mandatory services and carry forwarding the potentialities of 5G to superior and intelligent level. 6G network structure is determined to be dynamic, densely deployed and extremely heterogeneous, and when integrated with a high degree of Quality of Service (QoS) completely transforms the complex architecture into a seamless operating process of classical networks. The immense role of Artificial Intelligence (AI) and Machine Learning (ML) is required for improving the paradigm of 6G for learning information from uncertain and dynamic environments. This integration of AI and 6G resembles a double-edged sword since the application of AI may positively influences the privacy or security of 6G on one side, and negatively introduces the possibility of security infringement into 6G on the other side. In specific, the self-sustaining networks in 6G are obtained by guaranteed application of intelligent security attack mitigation schemes and proactive threat discovery approaches that facilitate end-to-end future network automation. In this Chapter, a comprehensive review of AI and ML-based security enforcement techniques are contributed for improving reliability during robust data dissemination in 6G communications. It presents consolidated and solidified role of AI and ML towards the enforcement of security in 6G networks. In addition, it also demonstrates the challenges and solutions that are handled by the inclusion of AI and ML-based attack mitigation approaches concerning energy and security-based ultra-massive access.

The arrival of the 6th generation (6G) technology will revolutionize the digital transformation of society. It offers speeds of several gigabits per second, with small latencies less than 5ms, ultra-reliable transmission medium, while it is able to handle a huge quantity of nodes simultaneously connected to the network. 6G will be accepted for commercial launch within 10 years. 6G will be industrialized in retort to progress the dispersed radio access network (RAN) and longing to use terahertz band to increase the number of users. On proposing an adaptive hybrid transmission (AHT) arrangement about 6G with mobile internet protocol television network (MIPTVN), the proposed algorithm uses a hybrid mechanism. The AHT mechanism is the combination of the multichannel multicast and unicast process which increases the service over service probability of blocking the service and reduces the overall bandwidth consumption of MIPTVN on demand over 6G.