Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing

Resource assignment and scheduling based on a two-phase metaheuristic for cropping system

This paper proposes a resource assignment and scheduling based on a two-phase metaheuristic for a long-term cropping schedule. The two-phase metaheuristic performs the optimization of resources assignment and scheduling based on a simulated annealing (SA), a genetic algorithm (GA) and a hybrid Petri nets model. The initial and progressive states of farmlands and resources, moving sequence of machinery, cooperative work, and deadlock removal have been well handled in the proposed approach. In the computational experiment, the schemes of emphasizing the resource assignment optimization, initializing the population of the GA with chromosomes sorted by the waiting time, and inheriting the priority list from tasks in the previous resources assignment improved the evolution speed and solution quality. The simulated result indicated that the formulated schedule has a high ratio of resource utilization in sugarcane production. The proposed approach also contributes a referential scheme for applying the metaheuristic approach to other crop production scheduling

Hybrid Petri nets modeling for farm work flow

This paper introduces hybrid Petri nets into modeling for farm work flow in agricultural production. The main emphasis is on the construction of an adequate model for designing practical farm work planning for agriculture production corporations. Hybrid Petri nets conventionally comprise a continuous part and a discrete part. The continuous part mainly models the practical work in the farmland, and the discrete part mainly represents the status changes in resources such as machinery and labor. The proposed model also models the present status or undesirable breaks during the farming process. Moreover, in this paper, the approach of formulating the farm work planning problem based on the model is suggested. The simulated results reveal that the hybrid Petri nets model is promising for exactly describing the farming process and reallocating resources in the presence of uncertainties. The proposed model serves as a referential model for farm work planning and it promotes the development of a corresponding optimization algorithm under uncertain environments

Mechanism of Increasing the Permeability of Water-Bearing Coal Rock by Microwave Steam Explosion

Microwave heating of water-bearing coal can promote pore water evaporation. The pores are broken under the action of steam pressure, increasing the permeability of the coal. This study is aimed at investigating the mechanism of permeability improvement of water-bearing coal rock by microwave steam explosion. First, a microwave oven was used to irradiate and heat five groups of coal rock with different water contents; the NMR test was then conducted on the heated sample. Second, the internal vapor pressure and temperature changes during the heating process were obtained through the T-connector for samples with different water contents. Finally, a numerical experiment was used to explore the deformation characteristics of pores under three filling conditions. The experimental results showed that the total porosity increased significantly when the water content of coal increased from 0% to 8%, while the permeability increased by nearly 4.78 times. The extreme value of gas pressure inside the sample showed an increasing trend. The gas pressure and temperature were in line with the equation of state for an ideal gas during the rising phase. Numerical experiments showed that the pore boundary shrunk inward under vacuum conditions, and compressive stress appeared at the tip. The saturated humid air and liquid water conditions expanded the pore boundaries outward and caused tensile stress at the tip, with the latter being nearly 2.3 times larger than the former, making it more conducive to the development of pores. The findings of this study can be used as a reference value for the expansion of coalbed methane extraction technology