## 6 Open Research Issues  
Despite of the immense popularity of IoT and UAV based smart farming, there are few challenges that need to be addressed. This section presents the open research issues related to IoT and UAV for sustainable smart farming. * Hardware maintenance and limited energy resources: The perception layer's hardware are setup in harsh environment, like farms and mining field, which have extreme weather conditions like high temperature, rain, strong wind and extreme humidity, etc. As a result, the electronic circuits of these hardware devices get damaged. Therefore, stronger hardware devices need to be designed that will be less damaged by the harsh environment. Additionally, these devices operate on inadequate battery power consistently for a long period. Hence, alternative energy efficient solutions are required for the end devices because, in case of any program failure, instant battery replacement is complicated, especially in remote areas [35]. Authors in [90; 91] suggested energy efficient approaches for the network side; however, energy efficient solutions for the end devices are also desirable. * Security and privacy issues: The use of IoT contributes to widespread exposure to cyber security risks and vulnerabilities in smart farming. [PERSON] et al. in [92], introduced some key challenges for security and privacy in smart farming: access control and trust, data, network and compliance, and supply chain. The architecture of smart farming recognizes the high chance of cyber attacks, which needs to be addressed. A smart farm which is a highly connected system and generates a huge amount of data. Since most of the devices used in smart farming are unattended therefore, they can be easily targeted by the attacker. If the attacker is able to compromise a device, then, through that infected device, attacks can spread through the whole network and infected all other devices. In the literature, several machine learning assisted Intrusion Detection Systems (IDS) [93; 94] have been proposed to identify the infected device by analysing the network traffic. However, none of them have considered the smart farming specific solution. Therefore, smart farm-specific sophisticated access control and machine learning-based IDS development can be an effective solution in this regard. * Big data in smart farming: A massive volume of data with a wide variety are captured, stored and analysed for decision-making in IoT and UAV based smart farming. Big data are used to provide predictive insights in farming operation by providing real-time operational decisions. The scope of Big Data applications in Smart Farming goes beyond primary production; rather, it influences the entire food supply chain. The major issues with data analysis are data quality, intelligent processing and analysis, sustainable integration of Big Data sources, etc. [95]. The openness of the platform is also very important since it can empower farmers in their position in supply chains. * Weed detection and management: Weed control is an important aspect of horticultural crop management. Failure to adequately control weeds leads to reduced yields and product quality. Use of chemical and cultural control strategies can lead to adverse environmental impacts when not managed carefully, and effective weed management strategies that minimise environmental risks through strategic application of control measures can be expensive to implement. Hence, low cost smart tools for identification and mapping of weeds at early growth stages will contribute to more effective, sustainable weed management approaches. Existing studies [26; 96; 97] have shown some approaches to detect weeds using UAV images; however, they only could achieve less than 90% of accuracy, hence more accurate weed detection approaches are desirable. The authors in [4] presented a shielded band sprayer to spray herbicides in weed, avoiding to spray on crops, hence increase the food quality and reduce the use of plant protection products.