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Soil compaction is one of the main limiting factors for sustainable agriculture. Efficient methods for detecting the distribution of soil compaction at a field scale are urgently needed in precision agriculture. We evaluated the possibility to detect soil compaction from measurements of apparent electrical conductivity (ECa) by using electromagnetic induction (EMI) and maize traits at a ~1 ha field parcel. The soil was Luvisol, and soil compaction‐related properties, such as bulk density (BD), penetration resistance (PR), soil texture, soil salinity, soil moisture and maize traits, were measured at ten locations across the field based on ECa variability. In this site‐specific study, no good correlation between soil compaction properties (i.e. BD and PR) and ECa was found but soil clay content was positively related to ECa in the sublayer (P = 0.08). The variation in the unmanned aerial vehicle (UAV)‐derived maize canopy height was significantly negatively correlated with soil compaction and a negative relationship between dry above‐ground biomass and BD and PR existed at 25–50 cm layer. In addition, ECa and UAV‐derived maize canopy height was negatively correlated (Spearman's rank correlation coefficients of −0.67 in the 0–25 cm layer) under the rather dry conditions prevailing during this study. Overall, as could be expected, detecting soil compaction with ECa was challenging under heterogeneous terrains, while maize traits had a better correlation with spatial variability of soil compaction. Owing to its sensitivity to the integral variation of multiple soil properties, ECa was a good indicator of maize canopy height. [ABSTRACT FROM AUTHOR]

Summary: In wheat (Triticum aestivum L) and other cereals, the number of ears per unit area is one of the main yield‐determining components. An automatic evaluation of this parameter may contribute to the advance of wheat phenotyping and monitoring. There is no standard protocol for wheat ear counting in the field, and moreover it is time consuming. An automatic ear‐counting system is proposed using machine learning techniques based on RGB (red, green, blue) images acquired from an unmanned aerial vehicle (UAV). Evaluation was performed on a set of 12 winter wheat cultivars with three nitrogen treatments during the 2017–2018 crop season. The automatic system uses a frequency filter, segmentation and feature extraction, with different classification techniques, to discriminate wheat ears in micro‐plot images. The relationship between the image‐based manual counting and the algorithm counting exhibited high levels of accuracy and efficiency. In addition, manual ear counting was conducted in the field for secondary validation. The correlations between the automatic and the manual in‐situ ear counting with grain yield were also compared. Correlations between the automatic ear counting and grain yield were stronger than those between manual in‐situ counting and GY, particularly for the lower nitrogen treatment. Methodological requirements and limitations are discussed. Significance Statement: Ear density (ears m–2) is one of the main agronomical yield components of wheat. This study represents a novel contribution to the field of RGB image processing for plant phenotyping using unmanned aerial vehicle (UAV) platforms. By combining high‐resolution RGB imagery with an automatic ear classification and counting system, we have shown that it is possible to assess ear density with high precision from an aerial platform. This is the first published study successfully deploying this approach. [ABSTRACT FROM AUTHOR]

In perennial ryegrass breeding programmes, dry‐matter yield (DMY) of individual plots is monitored destructively at the different cuts or derived from non‐destructive canopy height measurements using devices like rising plate meters (RPM). These approaches both have constraints. Destructive sampling implies low temporal resolution, restraining the study of dry‐matter accumulation rates, while RPM measurements are influenced by the canopy structure and limit intra‐field variability identification. We present a phenotyping methodology, based on the use of an affordable RGB camera mounted on an unmanned aerial vehicle (UAV), to monitor the spatial and temporal evolution of canopy height and to estimate DMY. Weekly flights were carried out from April to October above a field comprising a diverse set of accessions. To test the capacity of UAV imagery to estimate canopy height, 8 ground control points and 28 artificial height references were placed at different locations. Accurate flights with an RMSE as low as 0.94 cm were achieved. In addition, canopy height was recorded using an RPM and destructive biomass samples were collected. Different models (linear, multiple linear, principal components, partial least squares regression and random forest) were used to predict DMY, and their performance was evaluated. The best estimations were obtained by combining variables including canopy height, vegetation indices and environmental data in a multiple linear regression (R2 = .81). All models built using UAV data obtained a lower RMSE than the one using RPM data. The approach presented is a possibility for breeders to incorporate new information in their selection process. [ABSTRACT FROM AUTHOR]