Conference Papers

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7616

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    Towards better irrigation management for vineyards based on machine learning algorithms and SHAP analysis: a case study in New Zealand
    (19/09/2022) Wei H-E; Grafton MC; Bretherton M; Irwin M; Sandoval E
    Monitoring and management of grapevine water status (GWS) over the critical period between flowering and veraison plays a significant role in producing grapes of premium quality. GWS monitoring is critical to vineyard management, and so determines which variables are the main drivers of GWS variation. The goal of this study is to provide viticulturists with an approach to simulate the complex relationship between canopy GWS with vegetation, weather, day of the year (DOY), and soil/terrain variables, along with the interpretation of these relationships. A case study done in Martinborough, New Zealand is used for illustration. A UAV was flown over two Pinot Noir vineyards to generate aerial images with 4.3 cm resolution, and the vegetation index, Transformed Chlorophyll Absorption Reflectance Index (TCARI), was computed for every sampled grapevine. Slope and elevation were extracted from a digital elevation model, and apparent electrical conductivity (ECa) was obtained from an EM38 survey. A local weather station provided continuous air temperature, humidity, rainfall, wind speed, and irradiance data, which were computed as variables at weekly and daily intervals. DOY was used to represent temporal trends along the growing season. Hierarchical clustering and three machine learning algorithms (elastic net, random forest regression, support vector regression) were used to regress predictors against stem water potential (Ψstem), measured by a pressure bomb and used as a proxy for GWS. Shapley Additive exPlanations (SHAP) analysis (a statistical tool that weighs the importance of each variable in a model) was used to interpret the relationship between selected predictors and Ψstem. Our results showed that the coefficient of determination (R2) of the best-performed model reached 0.7 when simulated by support vector regression using TCARI, DOY, and elevation as inputs. This study has provided proof of concept of developing regression models that would be beneficial for grapevine irrigation systems via continuous GWS monitoring, while the identification and clarification of the relationship between statistically dominant variables would assist in decision-making to attain optimal grape quality.
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    Characterization traffic induced compaction in controlled traffic farming (CTF) and random traffic farming (RTF) - A multivariate approach
    Raveendrakumaran B; Grafton MC; Jeyakumar P; Bishop P; Davies CE; Horne, D; Singh, R
    A field scale experiment was carried out in Pukekohe in 2020 under an annual grass crop season to characterize the subsoil compaction in controlled traffic farming (CTF) and random traffic farming systems (RTF). Soil penetration resistance (PR) measurements were taken in each field using a cone penetrometer fitted with a 100 mm2 60° top angle cone. Multivariate analysis was performed to identify penetration resistance by depth through cluster analysis and principal component analysis (PCA). Repeated measures ANOVA was performed on the penetration data using the mixed model procedure to determine the treatment effects. In RTF, the penetrometer values increased more rapidly with depth resulting in higher values being recorded from 20cm compared to CTF. In contrast, it was greater in CTF than in RTF at the subsurface (55-60cm). The differences in PR declined beyond 55cm depth at both sites. All depths showed that differences in soil PR were most apparent in the 5-40cm depth, with significant differences between CTF and RTF (P<0.0001). This shows that traffic management at both CTF and RTF sites caused significant changes in the 5-40cm depth. However, there were no differences in PR between CTF and RTF below 40cm and at 0-5cm depth (P >0.05) showing that the soil layers were homogeneous in both systems beyond 40cm depth. The propagation of subsurface compaction was identified at the deeper layer (40-60cm) in CTF systems whereas it was identified from shallower depths (25-55cm) in RTF system.
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    Evaluation of the use of UAV-derived Vegetation Indices and Environmental Variables for Grapevine Water Status Monitoring based on Machine Learning Algorithms and SHAP Analysis
    (1/06/2022) Wei H-E; Grafton MC; Bretherton M; Irwin M; Sandoval E; Horne, D; Singh, R
    Monitoring and management of grapevine water status (GWS) over the critical period between flowering and veraison plays a significant role in producing grapes of premium quality. The spatial and temporal variation of GWS, is conventionally characterized by using a pressure bomb. This is laborious and time-consuming, which limits the number of samples. Although unmanned aerial vehicles (UAVs) can provide mapping across the entire vineyards efficiently, most commercial UAV-based multispectral sensors do not contain a shortwave infrared band which makes the monitoring of GWS problematic. As GWS is an integrated response to vegetation characteristics, temporal trends, weather conditions, and soil/terrain data, it is assumed that these ancillary variables have the potential to enhance the capability of UAVs for GWS monitoring. The goal of this study is to explore whether and which of these ancillary variables may improve the accuracy of GWS estimation using UAV, and provide insights into the contribution that each selected variable contributes to the variation in GWS. A UAV was flown over two Pinot Noir vineyards in New Zealand to generate aerial images with 4.3 cm resolution, and 18 vegetation indices (VIs) were computed for every sampled grapevine. The strongly correlated VIs were used as the core input for later GWS modeling. Ancillary data included soil/terrain, weather, and temporal variables. Slope and elevation were extracted from a digital elevation model, and apparent electrical conductivity (ECa) was obtained from an EM38 survey. A local weather station provided continuous air temperature, humidity, rainfall, wind speed, and irradiance data, which were computed as variables at weekly and daily intervals. Day of the year (DOY) was used to represent the temporal trend along the growing season. Three machine learning algorithms (elastic net, random forest regression, and support vector regression) were used to regress the predictors against stem water potential (Ψstem), measured by a pressure bomb and used as a proxy for GWS. Shapley Additive exPlanations (SHAP) analysis (a statistical tool that weighs the importance of each variable in a model) was used to assess the relationship between selected variables and Ψstem. The results show that Transformed Chlorophyll Absorption Reflectance Index (TCARI) and Excess Green Index (ExG) are the best correlated VIs, but their correlation with Ψstem is poor (rTCARI = 0.6; rExG = 0.58). The coefficient of determination (R2) of the TCARI-based model increased from 0.35 to 0.7 when DOY and elevation were included as ancillary inputs. R2 of the ExG-based model increased from 0.3 to 0.74 when DOY, elevation, slope, ECa, and daily average windspeed, were included as ancillary inputs. Support vector regression was the best model to describe the relationship between Ψstem and selected predictors. This study has provided proof of the concept of developing GWS estimation models that potentially enhance the monitoring capacities of UAVs for GWS, as well as provide individual GWS mapping at the vineyard scale. This may enable growers to improve irrigation management, leading to controlled vegetative growth and optimized berry quality.