# New Developments in Smart Farming Applied in Sustainable Agriculture
## 2 An Overview of Published Articles
The random forest classifier was trained on colour features extracted from principal components analysis, and the economic model was utilised to determine whether acquiring an additional viewpoint was cost-effective. The dynamic viewpoint selection approach enhanced the classification accuracy by 6% for red peppers and 5% for yellow peppers, in comparison with the use of a single viewpoint. The economic costs were reduced by 52% (red) and 12% (yellow), thereby demonstrating the efficiency of selective viewpoint acquisition. The insights provided by this research are of significant value for the field of agricultural robotics, particularly in the context of optimising machine vision techniques for the automated harvesting of sweet peppers. A pivotal soil parameter that influences plant growth and yield is soil compaction, the measurement of which by conventional methods is extremely labour-intensive. An alternative approach involves the utilisation of geophysical data to predict soil mechanical parameters, with geophysical soil features being measured using scanners such as the Geonics EM38 conductivity meter. Machine learning methods can be employed to develop models for predicting soil mechanical parameters. In the research conducted by [PERSON] et al. (Contribution 6), the relationship between soil electrical properties (electrical conductivity and magnetic susceptibility) and soil compaction and shear stress was investigated. Multiple linear regression and ML approaches were applied to predict these soil properties. The findings indicate that neural network models, particularly the multilayer perceptron and radial basis function networks, can produce models of high accuracy, achieving a correlation coefficient (R) of 0.846 for soil compaction and 0.680 for shear stress. These findings have practical applications in precision agriculture, allowing for improved soil management and reduced energy losses in agricultural machinery operations. Another significant parameter influencing plant growth and yield is soil moisture. The study by [PERSON] et al. (Contribution 7) evaluates the effectiveness of a deep learning approach, specifically a Bidirectional Long Short-Term Memory (BiLSTM) model, in predicting the Fraction of Transpirable Soil Water (FTSW) in vineyards. The FTSW was estimated based on different input parameters, including relative humidity, reference evapotranspiration, rainfall, and vapour pressure deficit. The authors developed models of high accuracy for an independent test data set (R\({}^{2}\) of 87% and an RMSE of 10.36%). The results suggest that BiLSTM models are a reliable tool for predicting FTSW in vineyards, which can support irrigation management decisions and optimise water use efficiency. The study highlights the potential of deep learning in precision agriculture and the need for further research to adapt these models to different environmental conditions. Sustainable agriculture activities closely related to precision farming include robotic solutions and autonomous harvesters, supported by machine learning-based vision systems. The Nano Aerial Vehicle employed for pollination purposes (Nano Aerial Bee) (Contribution 8) is a particularly intriguing case study. The researchers propose a bio-inspired approach, involving the design of miniature drones capable of replicating natural pollination processes. The article presents the hardware implementation and a vision system based on YOLO neural networks trained for flower detection. The study concludes that while NAVs present a viable solution to pollination challenges, further research is necessary to enhance their efficiency, scalability, and environmental integration. The findings emphasise the potential for robotics to contribute to the sustainability of agricultural productivity in the context of pollinator decline. Another example of the use of Unmanned Aerial Vehicles in agriculture is drones for precision spraying of crop protection products.