Abstract:Increasing the ability to investigate plant functions and structure through non-invasive methods with high accuracy has become a major target in plant breeding and precision agriculture. Emerging approaches in plant phenotyping play a key role in unraveling quantitative traits responsible for growth, production, quality, and resistance to various stresses. Beyond fully automatic phenotyping systems, several promising technologies can help accurately characterize a wide range of plant traits at affordable costs and with high-throughput. In this review, we revisit the principles of proximal and remote sensing, describing the application of non-invasive devices for precision phenotyping applied to the protected horticulture. Potentiality and constraints of big data management and integration with "omics" disciplines will also be discussed.

To assess spatial variability at the very fine scale required by Precision Agriculture, different proximal and remote sensors have been used. They provide large amounts and different types of data which need to be combined. An integrated approach, using multivariate geostatistical data-fusion techniques and multi-source geophysical sensor data to determine simple summary scale-dependent indices, is described here. These indices can be used to delineate management zones to be submitted to differential management. Such a data fusion approach with geophysical sensors was applied in a soil of an agronomic field cropped with tomato. The synthetic regionalized factors determined, contributed to split the 3D edaphic environment into two main horizontal structures with different hydraulic properties and to disclose two main horizons in the 0–1.0-m depth with a discontinuity probably occurring between 0.40 m and 0.70 m. Comparing this partition with the soil properties measured with a shallow sampling, it was possible to verify the coherence in the topsoil between the dielectric properties and other properties more directly related to agronomic management. These results confirm the advantages of using proximal sensing as a preliminary step in the application of site-specific management. Combining disparate spatial data (data fusion) is not at all a naive problem and novel and powerful methods need to be developed.

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Salt stress is one of the most impactful abiotic stresses that plants must cope with. Plants’ ability to tolerate salt stress relies on multiple mechanisms, which are associated with biomass and yield reductions. Sweet pepper is a salt-sensitive crop that in Mediterranean regions can be exposed to salt build-up in the root zone due to irrigation. Understanding the physiological mechanisms that plants activate to adapt to soil salinization is essential to develop breeding programs and agricultural practices that counteract this phenomenon and ultimately minimize yield reductions. With this aim, the physiological and productive performances of Quadrato D’Asti, a common commercial sweet pepper cultivar in Italy, and Cazzone Giallo, a landrace of the Campania region (Italy), were compared under different salt stress treatments. Quadrato D’Asti had higher tolerance to salt stress when compared to Cazzone Giallo in terms of yield, which was associated with higher leaf biomass vs. fruit ratio in the former. Ion accumulation and profiling between the two genoptypes revealed that Quadrato D’Asti was more efficient at excluding chloride from green tissues, allowing the maintenance of photosystem functionality under stress. In contrast, Cazzone Giallo seemed to compartmentalize most sodium in the stem. While sodium accumulation in the stems has been shown to protect shoots from sodium toxicity, in pepper and/or in the specific experimental conditions imposed, this strategy was less efficient than chloride exclusion for salt stress tolerance.