The Application of an Unmanned Aerial System and Machine Learning Techniques for Red Clover-Grass Mixture Yield Estimation under Variety Performance Trials

A significant trend has developed with the recent growing interest in the estimation of aboveground biomass of vegetation in legume-supported systems in perennial or semi-natural grasslands to meet the demands of sustainable and precise agriculture. Unmanned aerial systems (UAS) are a powerful tool when it comes to supporting farm-scale phenotyping trials. In this study, we explored the variation of the red clover-grass mixture dry matter (DM) yields between temporal periods (one- and two-year cultivated), farming operations [soil tillage methods (STM), cultivation methods (CM), manure application (MA)] using three machine learning (ML) techniques [random forest regression (RFR), support vector regression (SVR), and artificial neural network (ANN)] and six multispectral vegetation indices (VIs) to predict DM yields. The ML evaluation results showed the best performance for ANN in the 11-day before harvest category (R2 = 0.90, NRMSE = 0.12), followed by RFR (R2 = 0.90 NRMSE = 0.15), and SVR (R2 = 0.86, NRMSE = 0.16), which was furthermore supported by the leave-one-out cross-validation pre-analysis. In terms of VI performance, green normalized difference vegetation index (GNDVI), green difference vegetation index (GDVI), as well as modified simple ratio (MSR) performed better as predictors in ANN and RFR. However, the prediction ability of models was being influenced by farming operations. The stratified sampling, based on STM, had a better model performance than CM and MA. It is proposed that drone data collection was suggested to be optimum in this study, closer to the harvest date, but not later than the ageing stage

Social science perspectives on managing agricultural technology

TechnologyAgricultural researchResource managementFarmer participationEvaluation

Social science perspectives on managing agricultural technology

Experiences of 15 social science research fellows who recount their roles in particular research projects at the International Agricultural Research Centers they were appointed. In addition to highlighting the contributions social scientists can make in the field of agricultural research, their papers offer a candid look at the kinds of work in which the Centers currently are engaged.Technology, Agricultural research, Resource management, Farmer participation, Evaluation, Farm Management, Research Methods/ Statistical Methods,

Julian Ernst Besag, 26 March 1945 -- 6 August 2010, a biographical memoir

Julian Besag was an outstanding statistical scientist, distinguished for his pioneering work on the statistical theory and analysis of spatial processes, especially conditional lattice systems. His work has been seminal in statistical developments over the last several decades ranging from image analysis to Markov chain Monte Carlo methods. He clarified the role of auto-logistic and auto-normal models as instances of Markov random fields and paved the way for their use in diverse applications. Later work included investigations into the efficacy of nearest neighbour models to accommodate spatial dependence in the analysis of data from agricultural field trials, image restoration from noisy data, and texture generation using lattice models.Comment: 26 pages, 14 figures; minor revisions, omission of full bibliograph

Drought tolerance phenotyping in crops under contrasting target environments: procedures and practices.

Introduction. Comparing target environment characteristics for drought tolerance phenotyping. Site selection criteria. Soil characteristics for establishment of a specific site area. Main steps for a specific site selection and establishment in the field. A specific site selection and establishment case study: Sete Lagoas (Minas Gerais), Janaúba (Minas Gerais) and Teresina (Piauí), Brazil. Procedures for monitoring and controlling water stress for drought tolerance phenotyping. Irrigation scheme selection. Conventional sprinkler system. Localised irrigation system. Linear moving system. Field calibration procedures for water application and distribution. Container setupe for conventional sprinkler and linear moving irrigation systems. Calculation of the uniformity of water distribution. Procedures for the evaluation of applied water depth and distribution. Irrigation water management. Soil moisture measurements on drought tolerance field trials. Crop water requirements in localised systems. Irrigation depth. Measurement of crop water stress. Pehnotyping cereals and legumes for drought tolerance. Structure, maintenance and management of a database and modelling for drought tolerance phenothyping.bitstream/item/177192/1/Separata-00859.pd

NU-Spidercam: A large-scale, cable-driven, integrated sensing and robotic system for advanced phenotyping, remote sensing, and agronomic research

Field-based high throughput plant phenotyping has recently gained increased interest in the efforts to bridge the genotyping and phenotyping gap and accelerate plant breeding for crop improvement. In this paper, we introduce a large-scale, integrated robotic cable-driven sensing system developed at University of Nebraska for field phenotyping research. It is constructed to collect data from a 0.4ha field. The system has a sensor payload of 30kg and offers the flexibility to integrate user defined sensing modules. Currently it integrates a four-band multispectral camera, a thermal infrared camera, a 3D scanning LiDAR, and a portable visible near-infrared spectrometer for plant measurements. Software is designed and developed for instrument control, task planning, and motion control, which enables precise and flexible phenotypic data collection at the plot level. The system also includes a variable-rate subsurface drip irrigation to control water application rates, and an automated weather station to log environmental variables. The system has been in operation for the 2017 and 2018 growing seasons. We demonstrate that the system is reliable and robust, and that fully automated data collection is feasible. Sensor and image data are of high quality in comparison to the ground truth measurements, and capture various aspects of plant traits such as height, ground cover and spectral reflectance. We present two novel datasets enabled by the system, including a plot-level thermal infrared image time-series during a day, and the signal of solar induced chlorophyll fluorescence from canopy reflectance. It is anticipated that the availability of this automated phenotyping system will benefit research in field phenotyping, remote sensing, agronomy, and related disciplines.ISSN:0168-1699ISSN:1872-710

COMMUNITY BASED RESOURCE PLANNING - Studies from Zimbabwe and Northern Australia

Community/Rural/Urban Development,