# New Developments in Smart Farming Applied in Sustainable Agriculture
## 2 An Overview of Published Articles
Such drones can detect weeds in crops and carry out precision spraying, minimising the negative impact on the environment and reducing the problem of herbicide-resistant weeds. An important component of such drones are vision systems that allow accurate weed classification. Deep neural networks are employed for this purpose, but their optimal training remains challenging. [PERSON] et al. (Contribution 9) put forward the proposition of utilising the Harris Hawks Optimisation algorithm as a sophisticated approach for weed detection among crops by employing drone-captured images and Convolutional Neural Networks (CNNs). The experimental outcomes demonstrate that the proposed method enhances classification accuracy, computational efficiency, and generalisation capability. The findings suggest that integrating metaheuristic algorithms with CNNs can significantly enhance precision agriculture by enabling automated weed detection, leading to reduced herbicide use and improved crop yields. Sustainable agriculture employs a range of technologies to simultaneously optimise resource use, environmental impact and production volumes. In a paper by [PERSON] et al. (Contribution 10) the authors demonstrate that optimised Heating, Ventilation, and Air Conditioning (HVAC) system designs can significantly enhance sustainability, reduce water waste, and improve overall energy efficiency in controlled-environment agriculture. The findings highlight the potential for sustainable mushroom farming to contribute to climate change mitigation and more eco-friendly food production systems. [PERSON] et al. (Contribution 11) propose a digitalization framework for agricultural value chains with a view to enhancing inclusivity and economic growth in agricultural communities. The research highlights the role of agricultural social networks (ASNs) in knowledge sharing, market access, and financial inclusion for small-scale farmers. Machine learning algorithms are very helpful in the development of precision agriculture. The quality of the solutions obtained using ML depends very much on the quantity and quality of the data utilised during the training process. The advent of the Internet of Things (IoT), satellite imagery, and farm management systems has greatly facilitated data access. However, the heterogeneity of these data poses a significant challenge. In addressing this challenge, [PERSON] et al. (Contribution 12) developed a data aggregation and conversion model specifically tailored for smart farming, with the aim of enhancing the processing of heterogeneous agricultural data from multiple sources. Another issue that has been identified in the context of acquiring data for ML models is the necessity of manual labelling. For large-scale time-series data, this process is both labour-intensive and prone to errors. [PERSON] et al. (Contribution 13) proposed a novel approach to diagnosing device status in smart agriculture. This approach utilises pseudo-labelling and time-series data analysis, achieving an accuracy of 89% in agricultural environments and a 30% reduction in data processing time. The use of technologies to support sustainable agriculture often requires additional costs. In order for farmers to be willing to incur these costs, they need to be confident that environmentally beneficial measures will also be economically beneficial. [PERSON] et al. (Contribution 14) proposed an application, namely ProFit, which provides a simple way to assess the profitability of precision agriculture applications. This type of application, equipped with a user-facilitated interface, has the potential to stimulate interest among farmers in the adoption of precision agriculture methodologies.