# New Developments in Smart Farming Applied in Sustainable Agriculture
###### Abstract  
We present a novel novel approach to the development of innovative technologies for the precise management of crops and livestock, reducing the use of energy as well as natural resources such as water, fertilisers and plant protection products. Agricultural engineering research work is supported by disciplines such as computer science, automation and robotics, or artificial intelligence. The achievements of engineering science are increasingly being implemented in modern agriculture [5, 6, 7, 8]. Sustainable agriculture is a response not only to the deleterious impact of agricultural production on water, soil, and air quality [9, 10, 11, 12], but also to the reduction in biodiversity. The introduction of large-scale farming with monocultures has been demonstrated to reduce the diversity of plants found in these areas, and is also a threat to animals. One example of this is pollinating insects, which have difficulty accessing food for most of the year due to monocultures. Within crop species, there is also a decline in diversity, because farmers prefer a limited number of varieties that are easy to grow, disease-resistant, and economically viable. It is therefore very important to take care of agrobiodiversity, meaning diversity in the living organisms in agricultural systems (Contribution 1). A pivotal component of sustainable agriculture pertains to the precise optimisation of agro-technical treatments. Thanks to precision farming technology, it is possible to adjust the amount of fertiliser to the actual needs of the plants, thus reducing production costs and the negative impact on the ecosystem. This makes use of soil abundance and yield maps, as well as machines that implement variable fertiliser application. Performing treatments such as variable-depth tillage allows significant fuel savings while maintaining adequate soil quality for plant growth [14]. A significant element of crop production, namely plant protection, can be effectively supported by machines equipped with vision systems and artificial intelligence algorithms. These systems facilitate the identification of plant diseases, pest infestations, and the presence of weeds, thereby enabling the mechanical removal of weeds or the selective spraying of weeds. This approach facilitates an early response and precise crop management. The integration of robotics, vision systems, and machine learning (ML) methods also enables the development of autonomous harvesters. These machines have the capacity to harvest not only cereals, but also pressure-sensitive fruits such as strawberries [15]. The employment of drones facilitates the precise monitoringof agricultural fields and the application of crop protection products and fertilisers, in accordance with the specific requirements of the vegetation. The utilisation of artificial intelligence algorithms, encompassing ML, constitutes a pivotal element in the analysis of substantial agricultural data sets. The employment of predictive models facilitates the forecasting of meteorological conditions, the optimisation of sowing and harvesting dates, and the mitigation of losses incurred due to drought or crop diseases. These algorithms are capable of processing data generated by diverse sensor types, including those affixed to agricultural machinery, in addition to camera data and spectral data across a range of spectral domains [15]. Sustainable agriculture is an important line of research, as it allows increasing agricultural productivity while reducing the consumption of natural resources. This is of great importance given the negative environmental impact of crop and livestock production, and the need to feed the world's growing human population with resources reduced due to climate change. Therefore, the development of precision agriculture and AI technologies can contribute to the further development of sustainable agricultural practices.