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Reducing deep learning network structure through variable reduction methods in crop modeling

Artificial Intelligence in Agriculture 2021年第1期5卷 196-207页

作者： Babak Saravi A.Pouyan Nejadhashemi Prakash Jha Bo Tang Department of Biosystems and Agricultural Engineering Michigan State UniversityEast LansingMI 48824USA Department of Plant Soil and Microbial Sciences Michigan State UniversityEast LansingMI 48824USA Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification Kansas State UniversityManhattanKS 66506USA Department of Electrical and Computer Engineering Mississippi State UniversityMS 39762USA

crop models are widely used to predict plant growth,water input requirements,and ***,existing models are very complex and require hundreds of variables to perform *** to these shortcomings,large-scale applications of crop models are *** order to address these limitations,reliable crop models were developed using a deep neural network(DNN)–a new approach for predicting crop *** addition,the number of required input variables was reduced using three common variable selection techniques:namely Bayesian variable selection,Spearman's rank correlation,and Principal Component Analysis Feature *** reduced-variableDNN modelswere capable of estimating future crop yields for 10,000,000 differentweather and irrigation scenarios while maintaining comparable accuracy levels to the original model that used all input *** establish clear superiority of the methodology,the results were also compared with a very recent feature selection algorithm called min-redundancy max-relevance(mRMR).The results of this study showed that the Bayesian variable selection was the best method for achieving the aforementioned ***,the final Bayesian-based DNN model with a structure of 10 neurons in 5 layers performed very similarly(78.6%accuracy)to the original DNN cropmodel with 400 neurons in 10 layers,even though the size of the neural network was reduced by *** effort can help promote sustainable agricultural intensifications through the large-scale application of crop models.

关键词： Deep learning Artificial intelligent Variable reduction crop modeling Yield prediction Irrigation

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Coupling the 4M crop model with national geo-databases for assessing the effects of climate change on agro-ecological characteristics of Hungary

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International Journal of Digital Earth 2014年第5期7卷 391-410页

作者： Nandor Fodor Laszlo Pasztor Tamas Nemeth Institute for Soil Science and Agricultural Chemistry Centre for Agricultural ResearchHungarian Academy of SciencesBudapestHungary Hungarian Academy of Sciences BudapestHungary

The 4M crop model was used to investigate the prospective effects of climate change on the agro-ecological characteristics of *** model was coupled with a detailed meteorological database and spatial soil information systems covering the whole territory of ***-specific model parameters were determined by inverse *** meteorological data were produced from the present meteorological data by combining a climate change scenario and a stochastic weather *** the available and the generated data,the present and the prospective agro-ecological characteristics of Hungary were *** to the simulation results,average yields will decrease considerably(-30%)due to climate *** rate of nitrate leaching will prospectively decrease as *** fluctuations of both the yields and the annual nitrate leaching rates will most likely increase approaching the end of the twenty-first *** the basis of the simulation results,the role of autumn crops is likely to become more significant in *** achieved results can be generalized for more extended regions based on the concept of spatial(geographical)analogy.

关键词： agro-ecological features crop modeling climate change effects spatial soil information systems

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Validation of Growth and Nutrient Uptake Models for Tomato on a Gravelly South Florida Soil Under Greenhouse Conditions

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Pedosphere 2011年第1期21卷 46-55页

作者： D. A. CHIN X. H. FAN Y. C. LI Department of Civil and Environmental Engineering University of Miami Coral Gables FL 33124 (USA) Tropical Research and Education Center Soil and Water Science Department University of Florida Homestead FL 33031 (USA)

The Soil and Water Assessment Tool(SWAT) has been widely used throughout the world to model crop growth and nutrient uptake in various types of soils.A greenhouse experiment was performed to validate the process equations embedded in SWAT for describing the growth and nutrient uptake of tomatoes in south *** scaled growth curve of greenhouse-grown tomatoes was in close agreement with the theoretical model for field conditions,with the scaling factors being the maximum canopy height and the potential heat ***,the scaled leaf area index(LAI) growth curve and the scaled root depth curve for greenhousegrown tomatoes agreed with the SWAT functions,with the scaling factors being the maximum LAI and maximum root *** greenhouse experiment confirmed that the growth of biomass is a linear function of the intercepted photosynthetically active *** fractions of nutrients in the plant biomass under greenhouse conditions were found to be on the order of 60% of those fractions observed in the *** of the initial P distribution(0.2 mg kg -1),initial ratio of mineral stable P to mineral active P(50:1),and initial ratio of humic N to humic P(2.4:1) were determined from soil measurements and can be used for field *** conventional saturation-excess model for soil-water percolation was used to predict the movement of water in the top 10 cm of the greenhouse containers and the results agreed well with measurements.

关键词： container plants crop modeling Krome soil nitrogen phosphorus

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Sensitivity Analysis of the ALMANAC Model's Input Variables

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Agricultural Sciences in China 2002年第7期1卷 757-764页

作者： James R. Kiniry Jimmy R. Williams Uaited state Department of Agriculture Agricultural Research Service/Grassland Soil andWater Research Lab Texas 76502 U.S. AUaited state Department of Agriculture Agricultural Research Service/Grassland Soil andWater Research Lab Texas 76502 U.S. A

crop models often require extensive input data sets to realistically simulate crop growth. Development of such input data sets can be difficult for some model users. The objective of this study was to evaluate the importance of variables in input data sets for crop modeling. Based on published hybrid performance trials in eight Texas counties, we developed standard data sets of 10-year simulations of maize and sorghum for these eight counties with the ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria) model. The simulation results were close to the measured county yields with relative error only 2.6% for maize, and - 0.6% for sorghum. We then analyzed the sensitivity of grain yield to solar radiation, rainfall, soil depth, soil plant available water, and runoff curve number, comparing simulated yields to those with the original, standard data sets. Runoff curve number changes had the greatest impact on simulated maize and sorghum yields for all the counties. The next most critical input was rainfall, and then solar radiation for both maize and sorghum, especially for the dryland condition. For irrigated sorghum, solar radiation was the second most critical input instead of rainfall. The degree of sensitivity of yield to all variables for maize was larger than for sorghum except for solar radiation. Many models use a USDA curve number approach to represent soil water redistribution, so it will be important to have accurate curve numbers, rainfall, and soil depth to realistically simulate yields.

关键词： Sensitivity analysis crop modeling Sorghum Maize Runoff curve number Plant available water

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