Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed

Linking Remote Sensing with APSIM through Emulation and Bayesian Optimization to Improve Yield Prediction

The enormous increase in the volume of Earth Observations (EOs) has provided the scientific community with unprecedented temporal, spatial, and spectral information. However, this increase in the volume of EOs has not yet resulted in proportional progress with our ability to forecast agricultural systems. This study examines the applicability of EOs obtained from Sentinel-2 and Landsat-8 for constraining the APSIM-Maize model parameters. We leveraged leaf area index (LAI) retrieved from Sentinel-2 and Landsat-8 NDVI (Normalized Difference Vegetation Index) to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different calibration sites in the U.S. Midwest. The novelty of the current study lies in its approach in providing a mathematical framework to directly integrate EOs into process-based models for improved parameter estimation and system representation. Thus, a time variant sensitivity analysis was performed to identify the most influential parameters driving the LAI (Leaf Area Index) estimates in APSIM-Maize model. Then surrogate models were developed using random samples taken from the parameter space using Latin hypercube sampling to emulate APSIM’s behavior in simulating NDVI and LAI at all sites. Site-level, global and hierarchical Bayesian optimization models were then developed using the site-level emulators to simultaneously constrain all parameters and estimate the site to site variability in crop parameters. For within sample predictions, site-level optimization showed the largest predictive uncertainty around LAI and crop yield, whereas the global optimization showed the most constraint predictions for these variables. The lowest RMSE within sample yield prediction was found for hierarchical optimization scheme (1423 Kg ha−1) while the largest RMSE was found for site-level (1494 Kg ha−1). In out-of-sample predictions for within the spatio-temporal extent of the training sites, global optimization showed lower RMSE (1627 Kg ha−1) compared to the hierarchical approach (1822 Kg ha−1) across 90 independent sites in the U.S. Midwest. On comparison between these two optimization schemes across another 242 independent sites outside the spatio-temporal extent of the training sites, global optimization also showed substantially lower RMSE (1554 Kg ha−1) as compared to the hierarchical approach (2532 Kg ha−1). Overall, EOs demonstrated their real use case for constraining process-based crop models and showed comparable results to model calibration exercises using only field measurements

New APSIM-Sugar features and parameters required to account for high sugarcane yields in tropical environments

Sugarcane in field plot experiments in tropical Brazil (Guadalupe, Piaui State, 6.6 degrees S), produced very high yields under non-limiting water and nutrients. Mean stalk dry mass at 8, 11.5 and 15 months were 40, 51 and 70 t/ha respectively for six varieties and six planting dates. These yields were explained by high but not excessive temperatures allowing the canopy to close after 73 days on average. Substantial changes were required to enable the APSIM-Sugar model to simulate canopy and yield gain processes in Brazilian genotypes for the purpose of optimising variety, planting and harvest date options. A new modelling feature was proposed to deal with the observed growth slowdown when crop was about 7-8 months old and dry mass yields higher than 40 t/ha. All new parameters and features were validated with independent experiments as well as with the original dataset used for developing APSIM-Sugar. Future studies involving irrigation, yield gap analysis and climate change in environments where high yields are expected, should consider these modifications

Biometrical Models for Predicting Future Performance in Plant Breeding.

The plant breeding process begins with the selection of parents and crosses. Promising progeny from these crosses progress through a series of selection stages that typically culminate in multi-environment trials. I evaluated best linear unbiased predictors (BLUP), other predictors and prediction models at the initial (cross prediction), early replicated testing and late (multi-location) stages of a sugarcane breeding selection cycle. Model and predictor accuracy was assessed in the first two stages by using cross-validation procedures. I compared statistical models of progeny test data in their ability to predict the cross performance of untested sugarcane crosses. Random parental effect predictors and a random cross effect predictors were compared to mid-parent values (MPV) derived from a fixed female-male parental effect model. The cross effect model was evaluated with and without incorporating the genetic relationships among tested crosses into the BLUP derivation. Models with BLUP-based predictors showed smaller mean square prediction error and higher fidelity of top cross identification than the MPV for all traits evaluated. The MP-BLUP was consistently the best one. Prediction of per se (genotype) performance is needed during the selection process and requires combining information from different trials. The study investigated three mixed models involving three versions of BLUPs estimated under different strategies, a fixed least squares genotype means model, and four check-based methods for combining information at early replicated stages. BLUP-based predictors were superior to the currently used predictor (average percent of check cultivar). In addition, BLUP accuracy was not dependent on check values. In later selection stages, when few and highly selected genotypes are evaluated, genotype effects may be assumed fixed. By assuming genotype-by-environment interaction effects as random, the modeling of the covariance matrix allowed direct estimation of stability and genotype-by-environment measures. Closely related mixed models involving covariance parameters related with genotype-by-environment interaction were estimated. The covariance structure of the observations under the mixed models adjusted the genotype mean separation. Stability parameters were integrated into broad (across environment) and narrow (environment specific) inferences about genotype yield performances. A procedure to obtain visual representation of the genotype-by-environment interaction (BIPLOT) under a mixed AMMI model was also derived

Mekong Basin Focal Project: Synthesis report

The Mekong Basin Focal Project aims were to assess water use, water productivity and water poverty in the basin, and analyse the opportunities and risks of change in water management that influences water poverty. The main issue facing the Lower Mekong is not water availability (except for seasonally in certain areas such as northeast Thailand) but the impact of changed flows (which may result from dam or irrigation development or climate change) on ecology, fish production, access to water and food security. Poverty is generally decreasing in the Mekong, but the poorer people are not sharing in the improvements. Water governance and sharing of benefits is a key challenge for the Mekong

Using the soil and water assessment tool to simulate the pesticide dynamics in the data scarce Guayas River Basin, Ecuador

Agricultural intensification has stimulated the economy in the Guayas River basin in Ecuador, but also affected several ecosystems. The increased use of pesticides poses a serious threat to the freshwater ecosystem, which urgently calls for an improved knowledge about the impact of pesticide practices in this study area. Several studies have shown that models can be appropriate tools to simulate pesticide dynamics in order to obtain this knowledge. This study tested the suitability of the Soil and Water Assessment Tool (SWAT) to simulate the dynamics of two different pesticides in the data scarce Guayas River basin. First, we set up, calibrated and validated the model using the streamflow data. Subsequently, we set up the model for the simulation of the selected pesticides (i.e., pendimethalin and fenpropimorph). While the hydrology was represented soundly by the model considering the data scare conditions, the simulation of the pesticides should be taken with care due to uncertainties behind essential drivers, e.g., application rates. Among the insights obtained from the pesticide simulations are the identification of critical zones for prioritisation, the dominant areas of pesticide sources and the impact of the different land uses. SWAT has been evaluated to be a suitable tool to investigate the impact of pesticide use under data scarcity in the Guayas River basin. The strengths of SWAT are its semi-distributed structure, availability of extensive online documentation, internal pesticide databases and user support while the limitations are high data requirements, time-intensive model development and challenging streamflow calibration. The results can also be helpful to design future water quality monitoring strategies. However, for future studies, we highly recommend extended monitoring of pesticide concentrations and sediment loads. Moreover, to substantially improve the model performance, the availability of better input data is needed such as higher resolution soil maps, more accurate pesticide application rate and actual land management programs. Provided that key suggestions for further improvement are considered, the model is valuable for applications in river ecosystem management of the Guayas River basin

Statistical data mining algorithms for optimising analysis of spectroscopic data from on-line NIR mill systems

Justin Sexton investigated techniques to identify atypical sugarcane from spectral data. He found that identifying atypical samples could help remove bias in estimates of CCS. His results can be used to track occurrences of atypical cane or improve quality estimates providing benefits at various stages along the industry value chain

Evaluating and improving crop growth models for simulating genotype-by-environment interactions in sugarcane

Thesis (PhD (Agronomy))--University of Pretoria, 2023.In his thesis, entitled “Evaluating and improving crop growth models for simulating genotype-by-environment interactions in sugarcane”, Matthew Jones has enhanced our capacity to assist sugarcane breeding using crop growth simulation models. Matthew presents an analysis of genotype, environment and genotype-by-environment (GxE) effects in an international sugarcane multi-environment trial – the first study of its kind in sugarcane. As part of this analysis, a novel approach is used to assess the adequacy of established simulation concepts to account for genotypic control of plant process responses to environmental factors. This work is then expanded comprehensively to assess three sugarcane crop growth models for their abilities to simulate genotype performance in different environments. An important finding was that the duration of the germination phase strongly influenced subsequent canopy development and biomass growth. The thesis further describes the development of a new crop model, CaneGEM, to address weaknesses in existing models. Canopy development, biomass growth and biomass partitioning are simulated using a source-sink approach, enabling dynamic interaction between these processes – a necessity for realistic simulation of GxE interaction effects. CaneGEM showed improved capability for predicting GxE interaction effects at plant process level. A demonstration of the CaneGEM model revealed the potential to improve biomass yields via genotypic adaptations to cooler temperatures. Additionally, this study showed both the importance of the duration of germination phase in driving GxE interaction effects in canopy development and biomass yields, and some of the challenges involved in predicting this accurately.International Consortium of Sugarcane ModellingSouth African Sugarcane Research InstituteZimbabwe Sugar Association Experiment StationCentre de Coopération Internationale en Recherche Agronomique pour le DéveloppementSugar Cane Growers Cooperative from FloridaPlant Production and Soil SciencePhD (Agronomy)Unrestricte