# New Developments in Smart Farming Applied in Sustainable Agriculture
## 2 An Overview of Published Articles  
A significant trend in scientific research concerning precision farming techniques for sustainable agriculture is the development of methodologies for determining the optimal parameters for agricultural operations. The objective of this is to minimise energy consumption and negative environmental impact. For instance, in Contribution 2, the researchers examined the impact of moisture content (42%, 56%, and 69%), forage length (183 mm (chopped) and 312 mm (long)), and baling pressure (0.9 MPa, 1.4 MPa, and 1.8 MPa) on the density of haylarge bales. This study investigated the implications of these factors on fermentation, storage, and transport performance. Furthermore, a series of experiments were conducted to evaluate the impact of a silage additive on the compaction of bales. Drawing upon the findings of these experiments, the authors reported the optimal conditions for the baling of haylarge, with the objective of maximising density and storage efficiency. These conditions entailed a moisture content of 56%, forage lengths of 183 mm (chopped), and a pressing pressure of 1.8 MPa, with the intention of facilitating fermentation, reducing spoilage, and enhancing transport efficiency. In the contemporary context of agricultural practice, achieving optimal liquid distribution and uniformity during the process of spraying is a primary objective. Contribution 3 presents a model based on optimisation results, providing a practical framework for the characterisation of the distribution of liquid sprayed with single-stream nozzles. The model facilitates the control of nozzle operation and the assessment of wear, thereby ensuring uniform application. Experiments were conducted using anti-drift, air-induction, and standard nozzles. The model demonstrated strong agreement with experimental data (R\({}^{2}\) > 0.95), thus confirming its applicability in improving spraying uniformity. Another important trend in precision agriculture is the search for alternative, rapid methods for assessing soil and plant physico-chemical and biological parameters. These methods often combine measurement techniques with artificial intelligence methods, and allow accurate determination of selected parameters faster and cheaper than traditional laboratory methods. [PERSON] et al. (Contribution 4) investigated the potential of hyperspectral remote sensing to estimate nutrient levels in cowpea (_Vigna unquiculata_) leaves, focusing on the content of phosphorus, potassium, calcium, and zinc across different phenological stages (V4, R6, and R9). Leaf reflectance spectra in the range 350-2500 nm were measured using a spectroradiometer. The study utilised three modelling methods--single-band models, band ratio models, and Partial Least Squares Regression (PLSR)--to identify relationships between spectral data and nutrient levels in leaves. The findings revealed that phosphorus, potassium, and zinc levels diminished during the crop development phase, while calcium levels remained stable or increased, presumably due to its constrained mobility within plant tissues. The study concluded that the accuracy of the models was contingent on the phenological stage. Generally, PLSR produced the most accurate models, with high quality for estimation of phosphorus, potassium, and calcium. However, zinc prediction remains challenging. The vision-based method of red and yellow sweet pepper maturity classification was proposed by [PERSON] et al. (Contribution 5). The rule-based method for dynamically selecting viewpoints aims to improve classification accuracy while minimising economic costs. The research focuses on integrating next-best-view planning, where an additional viewpoint is captured only if it is profitable, based on misclassification risks. Sweet peppers were classified into three maturity stages based on RGB-D camera imaging.