Review:New sensors and data-driven approaches—A path to next generation phenomics

At the 4th International Plant Phenotyping Symposium meeting of the International Plant Phenotyping Network (IPPN) in 2016 at CIMMYT in Mexico, a workshop was convened to consider ways forward with sensors for phenotyping. The increasing number of field applications provides new challenges and requires specialised solutions. There are many traits vital to plant growth and development that demand phenotyping approaches that are still at early stages of development or elude current capabilities. Further, there is growing interest in low-cost sensor solutions, and mobile platforms that can be transported to the experiments, rather than the experiment coming to the platform. Various types of sensors are required to address diverse needs with respect to targets, precision and ease of operation and readout. Converting data into knowledge, and ensuring that those data (and the appropriate metadata) are stored in such a way that they will be sensible and available to others now and for future analysis is also vital. Here we are proposing mechanisms for “next generation phenomics” based on our learning in the past decade, current practice and discussions at the IPPN Symposium, to encourage further thinking and collaboration by plant scientists, physicists and engineering experts

Maximal Efficiency of PSII as a Marker of Sorghum Development Fertilized With Waste From a Biomass Biodigestion to Methane

The aim of experiments was to investigate a maximal efficiency of PSII, as a marker indicating growth, vigor, energetic value and physiological activity of sorghum fertilized with wastes from a biomass biodigestion to methane in a distillery integrated with a biogas plant using corn grains as substrate. The sorghum plants grown outdoor in different climate and in pots and in field were fertilized with different doses of the waste or Apol-humus – a soil improver and Stymjod – a nano-organic-mineral fertilizer. The maximal efficiency of PSII, in comparison with plant growth and health, chlorophyll content, gas exchange, activity of selected enzymes, element content in leaves and energetic value were studied. The wastes applied to soil resulted in increased maximal efficiency of PSII and the doses of 30 m3 ha-1 and 40–50 m3 ha-1 of the non-centrifuged and centrifuged ones, respectively, were most efficient. This enhancement was associated with the increased kinetics of plant growth, their health, fresh and dry biomass and physiological activity of plants as evidenced by activity of acid and alkaline phosphatase, RNase and dehydrogenase, as well as by gas exchange: net photosynthesis, transpiration, stomatal conductance, intercellular CO2 concentration and index of chlorophyll content in leaves. The fertilization with Apol-humus and Stymjod additionally increased maximal photochemical efficiency of PSII and plant development, biomass yield and physiological activity. The results indicate that waste from a biomass biodigestion to methane can be used as a natural fertilizer in sorghum crops and this ensures their recycling and environmental protection. The measurement values of maximal efficiency of PSII were proportionally to the vigor, growth and physiological activity of the plants. The obtained results indicate that the maximal efficiency of PSII in sorghum plants is a non-destructive method for defining the degree of growth and may be used as a marker of plant vigor and health, development and physiological activity expressed by gas exchange and activity of selected enzymes

Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits

The early detection of Xylella fastidiosa (Xf) infections is critical to the management of this dangerous plan pathogen across the world. Recent studies with remote sensing (RS) sensors at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, further work is needed to integrate remote sensing in the management of plant disease epidemics. Here, we research how the spectral changes picked up by different sets of RS plant traits (i.e., pigments, structural or leaf protein content), can help capture the spatial dynamics of Xf spread. We coupled a spatial spread model with the probability of Xf-infection predicted by a RS-driven support vector machine (RS-SVM) model. Furthermore, we analyzed which RS plant traits contribute most to the output of the prediction models. For that, in almond orchards affected by Xf (n = 1426 trees), we conducted a field campaign simultaneously with an airborne campaign to collect high-resolution thermal images and hyperspectral images in the visible-near-infrared (VNIR, 400–850 nm) and short-wave infrared regions (SWIR, 950–1700 nm). The best performing RS-SVM model (OA = 75%; kappa = 0.50) included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator (Tc), alongside pigments and structural parameters. Leaf protein content together with NIs contributed 28% to the explanatory power of the model, followed by chlorophyll (22%), structural parameters (LAI and LIDFa), and chlorophyll indicators of photosynthetic efficiency. Coupling the RS model with an epidemic spread model increased the accuracy (OA = 80%; kappa = 0.48). In the almond trees where the presence of Xf was assayed by qPCR (n = 318 trees), the combined RS-spread model yielded an OA of 71% and kappa = 0.33, which is higher than the RS-only model and visual inspections (both OA = 64–65% and kappa = 0.26–31). Our work demonstrates how combining spatial epidemiological models and remote sensing can lead to highly accurate predictions of plant disease spatial distribution.Data collection was partially supported by the European Union's Horizon 2020 research and innovation program through grant agreements POnTE (635646) and XF-ACTORS (727987). R. Calderón was supported by a post-doctoral research fellowship from the Alfonso Martin Escudero Foundation (Spain)

Digital phenotyping and genotype-to-phenotype (G2P) models to predict complex traits in cereal crops

The revolution in digital phenotyping combined with the new layers of omics and envirotyping tools offers great promise to improve selection and accelerate genetic gains for crop improvement. This chapter examines the latest methods involving digital phenotyping tools to predict complex traits in cereals crops. The chapter has two parts. In the first part, entitled “Digital phenotyping as a tool to support breeding programs”, the secondary phenotypes measured by high-throughput plant phenotyping that are potentially useful for breeding are reviewed. In the second part, “Implementing complex G2P models in breeding programs”, the integration of data from digital phenotyping into genotype to phenotype (G2P) models to improve the prediction of complex traits using genomic information is discussed. The current status of statistical models to incorporate secondary traits in univariate and multivariate models, as well as how to better handle longitudinal (for example light interception, biomass accumulation, canopy height) traits, is reviewe

Improved nitrogen retrievals with airborne-derived fluorescence and plant traits quantified from VNIR-SWIR hyperspectral imagery in the context of precision agriculture

In semi-arid conditions, nitrogen (N) is the main limiting factor of crop yield after water, and its accurate quantification remains essential. Recent studies have demonstrated that solar-induced chlorophyll fluorescence (SIF) quantified from hyperspectral imagery is a reliable indicator of photosynthetic activity in the context of precision agriculture and for early stress detection purposes. The role of fluorescence might be critical to our understanding of N levels due to its link with photosynthesis and the maximum rate of carboxylation (Vcmax) under stress. The research presented here aimed to assess the contribution played by airborne-retrieved solar-induced chlorophyll fluorescence (SIF) to the retrieval of N under irrigated and rainfed Mediterranean conditions. The study was carried out at three field sites used for wheat phenotyping purposes in Southern Spain during the 2015 and 2016 growing seasons. Airborne campaigns acquired imagery with two hyperspectral cameras covering the 400–850 nm (20 cm resolution) and 950–1750 nm (50 cm resolution) spectral regions. The performance of multiple regression models built for N quantification with and without including the airborne-retrieved SIF was compared with the performance of models built with plant traits estimated by model inversion, and also with standard approaches based on single spectral indices. Results showed that the accuracy of the models for N retrieval increased when chlorophyll fluorescence was included (r2LOOCV ≥ 0.92; p < 0.0005) as compared to models only built with chlorophyll a + b (Cab), dry matter (Cm) and equivalent water thickness (Cw) plant traits (r2LOOCV ranged from 0.68 to 0.77; p < 0.005). Moreover, nitrogen indices (NIs) centered at 1510 nm yielded more reliable agreements with N concentration (r2 = 0.69) than traditional chlorophyll indices (TCARI/OSAVI r2 = 0.45) and structural indices (NDVI r2 = 0.57) calculated in the VNIR region. This work demonstrates that under irrigated and non-irrigated conditions, indicators directly linked with photosynthesis such as chlorophyll fluorescence improves predictions of N concentration.The authors gratefully acknowledge the financial support of the Spanish Ministry of Science and Education (MEC) for projects AGL2012-40053-C03-01, and AGL2012-35196 and the Junta de Andalucia for projects P12-AGR-2521 and P12-AGR-0482.Peer reviewe

Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits

The early detection of Xylella fastidiosa (Xf) infections is critical to the management of this dangerous plan pathogen across the world. Recent studies with remote sensing (RS) sensors at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, further work is needed to integrate remote sensing in the management of plant disease epidemics. Here, we research how the spectral changes picked up by different sets of RS plant traits (i.e., pigments, structural or leaf protein content), can help capture the spatial dynamics of Xf spread. We coupled a spatial spread model with the probability of Xf-infection predicted by a RS-driven support vector machine (RS-SVM) model. Furthermore, we analyzed which RS plant traits contribute most to the output of the prediction models. For that, in almond orchards affected by Xf (n = 1426 trees), we conducted a field campaign simultaneously with an airborne campaign to collect high-resolution thermal images and hyperspectral images in the visible-near-infrared (VNIR, 400–850 nm) and short-wave infrared regions (SWIR, 950–1700 nm). The best performing RS-SVM model (OA = 75%; kappa = 0.50) included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator (Tc), alongside pigments and structural parameters. Leaf protein content together with NIs contributed 28% to the explanatory power of the model, followed by chlorophyll (22%), structural parameters (LAI and LIDFa), and chlorophyll indicators of photosynthetic efficiency. Coupling the RS model with an epidemic spread model increased the accuracy (OA = 80%; kappa = 0.48). In the almond trees where the presence of Xf was assayed by qPCR (n = 318 trees), the combined RS-spread model yielded an OA of 71% and kappa = 0.33, which is higher than the RS-only model and visual inspections (both OA = 64–65% and kappa = 0.26–31). Our work demonstrates how combining spatial epidemiological models and remote sensing can lead to highly accurate predictions of plant disease spatial distribution

Modelling the drift of thermographic sensors UAV for efficient irrigation management

El uso civil de plataformas aéreas no tripuladas ha experimentado un notable aumento en la última década, siendo la agricultura una de las áreas que mayor interés ha despertado. La flexibilidad que ofrecen estas plataformas, permitiendo realizar vuelos sobre el cultivo en el preciso omento de interés generando estudios multi-temporales con técnicas de teledetección de muy alta resolución espacial. Estas aplicaciones están siendo posibles por la miniaturización de sensores que hace posible embarcarlos como carga de pago en estas plataformas. De este modo los sensores registran información de los cultivos en distintas regiones del espectro electromagnético, que es procesada para aplicaciones de agricultura de precisión. En función del tipo de sensor, su uso presenta un mayor o menor grado de madurez, beneficiando o limitando su uso. En el caso del uso de sensores termográficos, su uso aparece más limitado a consecuencia de la tecnología empleada si bien despierta un elevado interés tanto para su aplicación en la detección de enfermedades o evaluación de estrés hídrico en cultivos. Los sensores termográficos de uso civil se basan en una tecnología de microbolómetros no refrigerados, la cual presenta cambios continuos en la medida de temperatura. Esta inestabilidad genera una deriva en la adquisición de los valores de temperatura que debe ser corregida. Se presenta un método que permite calcular la deriva de cualquier sensor termográfico en función del tiempo.The civilian use of unmanned aerial platforms has experienced a remarkable interest in the last decade, with agriculture being one of the areas that has aroused most interest. The flexibility offered by these platforms, allowing flights over the crop at the precise moment of interest, makes it possible to carry out multi-temporal studies applying remote sensing techniques with very high spatial resolution. These applications are being made possible by the miniaturisation of sensors, which makes it possible to ship them as payloads on these platforms. In this way, sensors record crop information in different regions of the electromagnetic spectrum, which, once processed, are used in precision agriculture applications. Depending on the type of sensor, its use has a greater or lesser degree of maturity, benefiting or limiting its use. In the case of thermographic sensors, their use is more limited due to the technology used, although they are of great interest for their application in the detection of diseases or the evaluation of water stress in crops. Thermographic sensors for civilian use are based on uncooled microbolometer technology, which shows continuous changes in temperature measurement. This instability generates a drift in the acquisition of temperature values that must be corrected. A method is presented that allows the drift of any thermographic sensor to be calculated as a function of time

The use of earth observation multi-sensor systems to monitor and model Pastures: a case of Savannah Grasslands in Hluvukani Village, Bushbuckridge Local Municipality, Mpumalanga Province, South Africa

Grassland degradation associated with climate change and inappropriate grassland management has been characterized as a global environmental concern driving decreased grassland ecosystem's ecological functioning. More than 60% of South African grassland is degraded or permanently transformed to other land uses and nearly 2% properly conserved. Yet, grasslands are a major source of food for livestock grazing and provide material and non-material benefits to many livelihoods. Therefore, grassland above-ground biomass (AGB) estimation is crucial in planning and managing pastoral agriculture and the benefits derived from it. However, current grassland monitoring techniques used in rural smallholder livestock farms rely on conventional methods, which are destructive, labour-intensive, costly, and restricted to small areas. This study investigated the monitoring and modelling of protected grasslands biomass using current Earth observation systems (EOS), an approach, which is non-destructive, cost-effective, cover larger areas and is a time-saving alternative to conventional methods. Hence, the research objectives were: (i) to map the trends and advances in data and models used in the monitoring of grassland (pastures) with Earth observation systems, and (ii) to assess above-ground biomass estimation in semi-arid savannah grassland integrating Sentinel-1 and Sentinel-2 data with Machine-Learning. This goal was to assess if this approach could provide the requisite information, which could contribute to the long-term goal of developing a semi-automated system for data processing, and mapping grassland biomass to benefit local communities. For this investigation, it was crucial to understanding what research had achieved so far in this area of pasture management. An assessment of the Scopus database showed the recent developments in European Union (EU) programs and Sentinel missions, including statistical models and machine learning for monitoring grassland changes at multiple scales. However, Sentinel-1 and Sentinel-2 data, machine learning models, and variable importance techniques were applied for grassland AGB estimation. These techniques have been used in similar studies to determine optimum machine learning models, influential variables, and the capability of integrated Sentinel datasets for mapping grassland AGB, spatial distribution, and abundance. Results showed improved performance with the Random forest regression (RFR) model (R² of 34.7%, RMSE of 9.47 Mg and MAE of 7.68 Mg ). The study also observed optimum sensitivity of Difference Vegetation Index (DVI) and Enhanced Vegetation Index (EVI) in all three machine learning models for modelling grassland AGB estimation in the study area. A further, statistical comparison of all three machine learning models showed an insignificant difference in the predictive capacity for AGB in the study area with Gradient Boosting regression (GBR) model (R² of 27.7, RMSE of 9.97 Mg and MAE of 8.03 Mg ) and Extreme Gradient Boost Regression (XGBR) model (R² of 17.3%, RMSE of 10.66 Mg and MAE of 8.83 Mg ). The study revealed that an integration of Sentinel-1 and Sentinel-2 has improved capabilities for monitoring grassland AGB estimation. This research sheds light on the timely and cost-effective techniques for grassland management strategies to enhance or restore the ecological functioning of grassland ecosystems and promote community sustainability.Thesis (MSc) -- Faculty of Science and Agriculture, 202

The use of earth observation multi-sensor systems to monitor and model Pastures: a case of Savannah Grasslands in Hluvukani Village, Bushbuckridge Local Municipality, Mpumalanga Province, South Africa

Grassland degradation associated with climate change and inappropriate grassland management has been characterized as a global environmental concern driving decreased grassland ecosystem's ecological functioning. More than 60% of South African grassland is degraded or permanently transformed to other land uses and nearly 2% properly conserved. Yet, grasslands are a major source of food for livestock grazing and provide material and non-material benefits to many livelihoods. Therefore, grassland above-ground biomass (AGB) estimation is crucial in planning and managing pastoral agriculture and the benefits derived from it. However, current grassland monitoring techniques used in rural smallholder livestock farms rely on conventional methods, which are destructive, labour-intensive, costly, and restricted to small areas. This study investigated the monitoring and modelling of protected grasslands biomass using current Earth observation systems (EOS), an approach, which is non-destructive, cost-effective, cover larger areas and is a time-saving alternative to conventional methods. Hence, the research objectives were: (i) to map the trends and advances in data and models used in the monitoring of grassland (pastures) with Earth observation systems, and (ii) to assess above-ground biomass estimation in semi-arid savannah grassland integrating Sentinel-1 and Sentinel-2 data with Machine-Learning. This goal was to assess if this approach could provide the requisite information, which could contribute to the long-term goal of developing a semi-automated system for data processing, and mapping grassland biomass to benefit local communities. For this investigation, it was crucial to understanding what research had achieved so far in this area of pasture management. An assessment of the Scopus database showed the recent developments in European Union (EU) programs and Sentinel missions, including statistical models and machine learning for monitoring grassland changes at multiple scales. However, Sentinel-1 and Sentinel-2 data, machine learning models, and variable importance techniques were applied for grassland AGB estimation. These techniques have been used in similar studies to determine optimum machine learning models, influential variables, and the capability of integrated Sentinel datasets for mapping grassland AGB, spatial distribution, and abundance. Results showed improved performance with the Random forest regression (RFR) model (R² of 34.7%, RMSE of 9.47 Mg and MAE of 7.68 Mg ). The study also observed optimum sensitivity of Difference Vegetation Index (DVI) and Enhanced Vegetation Index (EVI) in all three machine learning models for modelling grassland AGB estimation in the study area. A further, statistical comparison of all three machine learning models showed an insignificant difference in the predictive capacity for AGB in the study area with Gradient Boosting regression (GBR) model (R² of 27.7, RMSE of 9.97 Mg and MAE of 8.03 Mg ) and Extreme Gradient Boost Regression (XGBR) model (R² of 17.3%, RMSE of 10.66 Mg and MAE of 8.83 Mg ). The study revealed that an integration of Sentinel-1 and Sentinel-2 has improved capabilities for monitoring grassland AGB estimation. This research sheds light on the timely and cost-effective techniques for grassland management strategies to enhance or restore the ecological functioning of grassland ecosystems and promote community sustainability.Thesis (MSc) -- Faculty of Science and Agriculture, 202