Feeding the growing global population requires an annual increase in food production. This requirement suggests an increase in the use of pesticides, which represents an unsustainable chemical load for the environment. To reduce pesticide input and preserve the environment while maintaining the necessary level of food production, the efficiency of relevant processes must be drastically improved. Within this context, this research strived to design, develop, test and assess a new generation of automatic and robotic systems for effective weed and pest control aimed at diminishing the use of agricultural chemical inputs, increasing crop quality and improving the health and safety of production operators. To achieve this overall objective, a fleet of heterogeneous ground and aerial robots was developed and equipped with innovative sensors, enhanced end-effectors and improved decision control algorithms to cover a large variety of agricultural situations. This article describes the scientific and technical objectives, challenges and outcomes achieved in three common crops.

In precision viticulture, the intra-field spatial variability characterization is a crucial step to efficiently use natural resources by lowering the environmental impact. In recent years, technologies such as Unmanned Aerial Vehicles (UAVs), Mobile Laser Scanners (MLS), multispectral sensors, Mobile Apps (MA) and Structure from Motion (SfM) techniques enabled the possibility to characterize this variability with low efforts. The study aims to evaluate, compare and cross-validate the potentiality and the limits of several tools (UAV, MA, MLS) to assess the vine canopy size parameters (thickness, height, volume) by processing 3D point clouds. Three trials were carried out to test the different tools in a vineyard located in the Chianti Classico area (Tuscany, Italy). Each test was made of a UAV flight, an MLS scanning over the vineyard and a MA acquisition over 48 geo-referenced vines. The Leaf Area Index (LAI) were also assessed and taken as reference value. The results showed that the analyzed tools were able to correctly discriminate between zones with different canopy size characteristics. In particular, the R² between the canopy volumes acquired with the different tools was higher than 0.7, being the highest value of R² = 0.78 with a RMSE = 0.057 m³ for the UAV vs. MLS comparison. The highest correlations were found between the height data, being the highest value of R² = 0.86 with a RMSE = 0.105 m for the MA vs. MLS comparison. For the thickness data, the correlations were weaker, being the lowest value of R² = 0.48 with a RMSE = 0.052 m for the UAV vs. MLS comparison. The correlation between the LAI and the canopy volumes was moderately strong for all the tools with the highest value of R² = 0.74 for the LAI vs. V_MLS data and the lowest value of R² = 0.69 for the LAI vs. V_UAV data.

This study shows a new methodological proposal for wine farm management, as a result of the progressive development of the technological innovations and their adoption. The study was carried out in Italy involving farmers, workers, or owners of wine farms who are progressively introducing or using precision agriculture technologies on their farm. The methodology proposed was divided in four stages (1. understanding the changes in action; 2. identifying the added value of Smart Farming processes; 3. verifying the reliability of new technologies; 4. adjusting production processes) that can be applied at different levels in vine farms to make the adoption of precision agriculture techniques and technologies harmonious and profitable. Data collection was carried out using a participant-observer method in brainstorming sessions, where the authors reflected on the significance of technology adoption means and how to put them in practice, and interviews, questionnaire surveys, diaries, and observations. Moreover, project activities and reports provided auxiliary data. The findings highlighted the issues of a sector which, although with broad investment and finance options, lacks a structure of human, territorial, and organizational resources for the successful adoption of technological innovations. The work represents a basis for the future development of models for strategic scenario planning and risk assessments for farmers, policymakers, and scientists.

This paper set out to explore the precision agriculture (PA)-training needs of students studying in agricultural universities in the Euro-Mediterranean region (Greece, Italy, Portugal and Spain). SPARKLE is a Knowledge Alliance Project, funded by the European Union (EU), and one of its main goals is to narrow the innovation divide between entrepreneurship and the effective application of sustainable PA. During the project, the research conducted in all countries in the Euro-Mediterranean region revealed differences in the PA-training needs of university students. Additionally, this paper set out to explore the socioeconomic characteristics of students that affect their interest and knowledge towards PA. Finally, this paper aimed to understand the scope, present status and strategies for improving PA training in agricultural universities in the Euro-Mediterranean region. The following descriptive statistics and two multivariate analysis techniques were used: Two-Step Cluster Analysis (TSCA) and Categorical Regression (CATREG). Results support the notion that the lack of “PA knowledge/interest” adds to the technological gap amongst university students, slow adoption of PA and lower levels of overall rural economic development. These findings will be used as the fundamental cognition for the development of a joint action plan and several other national plans in the selected regions.