FUZZY MODELING OF SOIL COMPACTION DUE TO AGRICULTURAL MACHINE TRAFFIC

Transcription

1 FUZZY MODELING OF SOIL COMPACTION DUE TO AGRICULTURAL MACHINE TRAFFIC Augusto Guilherme de Araújo, Agronomical Institute of Parana (IAPAR) and Agricultural Automation Laboratory / University of São Paulo (USP) Antonio Mauro Saraiva, amsaraiv@usp.br Agricultural Automation Laboratory / USP Abstract The adoption of agricultural conservation systems associated with large agricultural machines use and short traffic time for the main mechanized operations in crop systems in Brazil has resulted in considerable soil damage. The research objective is to develop a tool for soil compaction assessment which may help planning of mechanized operations in order to increase the sustainability of agricultural activity. The model, based in fuzzy logic, aims to predict the change in soil structure (bulk density, total porosity and penetration resistance) due to wheeling and to classify soil compaction level. The model has two inference systems. The first computes the soil structure changes and the second classifies the compaction level. Both of them have been built based on experimental data, collected in special designed field experiments, and expert knowledge provided by engineers with high experience on the subject, who evaluated, qualitatively, the init ial and final soil experimental conditions. These two sources of information were integrated using, initially, techniques to extract rules and membership functions from measurement data and then confront them with expert knowledge to modify and adjust the model 1. Introduction Intensive crop production systems with soybean and corn have grown rapidly in the last few years in Brazil and, at the same time, have been under pressure to adopt agricultural practices that can result in excessive soil compaction. Examples of these practices are: mechanized operations on soil with high water content; rapid increase in conservation agriculture which eliminated periodic tillage (plowing and harrowing) and increase in machine weight and capacity. Since soil changes induced by machine traffic compaction can lead to soil degradation, superficial water pollution and an increasing demand for no-renewable natural resources, it is fundamental to develop useful tools to evaluate the effects of a machine on soil and predict final soil conditions after traffic, in order to avoid serious soil compaction problems (Canillas & Salokhe, 2002). Soil compaction by machine traffic is a complex process with many interacting factors. For this reason, mathematical models have been developed to help to understand this phenomenon (Defossez & Richard, 2002). Much scientific knowledge has been accumulated on this subject, but not in a useful way for farm management decisions. Defining exactly what constitutes compacted soil is a difficult task due to the many uncertainties involved in the process. These uncertainties include a large diversity in plant and environmental response to soil compaction, spatial variability of soil attributes and measurement errors. 97

2 Like other soil processes, compaction cannot be adequately represented with discrete categories (crisp classification), since soil changes are continuous. In general, a specific soil condition cannot be clearly described as compacted or not compacted. Fuzzy logic provides a formal mathematical structure for analyzing complex processes where observations should be grouped in continuous classes (Zimmermann, 1996). Fuzzy modeling has been applied in many scientific and engineering fields, and represents a useful framework to deal with 1) the complexity of soil compaction processes, 2) the uncertainty due to measurement errors and imprecise boundaries and, 3) qualitative knowledge generally associated with site-specific soil compaction evaluation. Soil science presents many possibilities for fuzzy logic application according to McBratney & Odeh (1997). 2. Objective Development of a fuzzy logic-based model to estimate and classify soil compaction due to agricultural machine traffic in no-tillage soil management system. 3. Methodology The model has two fuzzy inference systems. The first aims to compute soil structure changes resulting from machine traffic and the second classifies soil compaction levels. Soil and machine attributes, such as bulk density, total porosity, water content, penetration resistance, axle load and wheel pressure are the input for the first model. The output variables are final soil bulk density, total porosity and penetration resistance, which are the attributes mainly used to describe soil compaction level. The prediction is made for soil layers of 10 cm to 40 cm in depth. The fuzzy membership function for each variable and the rule base were developed based on experimental data collected in experiments specially designed with this purpose. The algorithm for fuzzy inference system development called ANFIS and provided by MATLAB software was used to construct membership functions and the rule base. Subsequently, the system was tuned by expert knowledge elicitation. Since the software has several constraints, such as single output derived by weighted average defuzzification, each system output has a separate fuzzy system and corresponding ANFIS with the same inputs. The second inference system classifies soil compaction levels considering fuzzy soil compaction criteria. In this case, compaction classes are expressed as fuzzy sets and input variables are soil attributes estimated by the first inference system. The output is a classification scheme of soil compaction level. In this case, expert knowledge and qualitative analysis of soil profile after traffic were the basis for defining the parameters and structure of the output membership functions and system rule base. Associated with each compaction class is a suggestion for remedial actions to be taken. Part of experimental data was used for verification of the first inference system performance Experimental phase Three experiments were conducted at the experimental station of the Agronomical Institute of the State of Parana (IAPAR) in Londrina (23 23 S and W), Parana, Brazil. The soil was an Oxisol with 82% clay, 13% silt and 5% sand content. The field was cultivated using a direct seeding system for the last seventeen years, and the mulch over the soil surface was soybean residue. The machine traffic effects on soil physical attributes (total porosity, bulk density and penetration resistance) were evaluated for two levels of soil water content (wet and field capacity), three levels of axle load (3.8, 7.2 and 10.2 Mg), two levels of 98

3 inflation pressure of the tire (minimum and maximum recommended for the load) and two levels for number of tire passes (1 and 3 passes). The plots had 140 m 2 (5x28m) and the soil evaluations were made in three transections, located at 7, 14 and 21 meters from the beginning of the plot. In each transection, soil penetration resistance was measured in seven positions before and after machine passage, each at 5 cm to 45 cm depth, with a cone penetrometer from Spectrum Technologies, made according to ASAE standards; soil bulk density samples were collected in four positions in each transection, before and after machine passage, according to core method at depth intervals of , , and cm, and with a sampler designed by IAPAR (FIGURE 1); soil total porosity and water content (gravimetrical) were determined from bulk density samples. Soil trenches were dug before and after the experiment installation of 1.2x1.0x1.0m each, to observe the soil structures and qualitatively assess soil compaction effects using crop profile methodology (Tavares Filho et al, 1999) Knowledge acquisition Expert knowledge acquisition was developed during soil profile evaluations in each plot. A soil scientist with long experience in this matter carefully analyzed soil structures formed below the machine passage and tried to described soil compaction intensity. At the same time, hand held cone penetrometer measurements were made in each profile in a 5x5 cm grid, to 40 cm in depth. FIGURE 2 shows a soil profile after the measurements and a soil structure compacted by traffic. 4. Results First results are being evaluated at this time. Initially, only three inputs were considered to develop the model for bulk density estimation. These inputs, axle load, soil water content and initial soil bulk density, were selected as the most important for the compaction process, based on a bibliographic review on this matter. A correlation analysis made on experimental data confirmed this premise. The databases were used in the computational process described in the former section and two resulting models for average depths of 5 and 15 cm were developed. In both models, the subtractive clustering method was used to estimate the number of clusters and the clusters centers, to initialize the optimization process (ANFIS). A combination of back-propagation and least square estimation was used for membership parameter estimation. The model performance was evaluated by the determination coefficient (R 2 ), based on linear regression and by the root mean square error between the observed and estimated results. The 5 cm depth model was trained with 48 data pairs and tested with 15 data pairs. The model has 8 rules, average test error of and determination coefficient of The 15 cm depth model was trained with 46 data pairs and tested with 17 data pairs. It has 6 rules, average test error of and determination coefficient of Figures 3 and 4 shows the observed and estimated results for both models. The 15 cm depth model showed less data scattering around the linear regression equation, which was close to 1:1 line, and consequently presented a small root mean square error compared with the 5 cm depth model. In conservation systems, the superficial soil layer presents, in general, high soil physical attribute variability due to the soil mobilization of seeding operation which can reach 10 cm depth. This could partially explain the worse performance of the 5 cm depth model. The determination coefficient obtained for the two models means that they perform well in estimating about half of the observed results. Additional effort in tuning the inference 99

4 system, especially through expert knowledge elicitation to select and adjust membership functions parameters, may result in a better model performance. Simultaneously, more training data must be collected in a larger range of soil water content values to ensure a better generalization capability of the system. Although these initial models performance were not so good and there is clearly a need to tune the model, reduce noise data and increase the number of training data, fuzzy approach seems promising and could provide a prospective tool to assess machine traffic effects on soil. LITERATURE CANILLAS, E.C.; SALOKHE, V.M. A decision support system for compaction assessment in agricultural soils. Soil & Tillage Research, Amsterdam, v.65, p , DEFOSSEZ, P.; RICHARD, G. Models of soil compaction due to traffic and their evaluation. Soil & Tillage Research, Amsterdam, v.67, p.41-64, McBRATNEY, A.B.; ODEH, I.O.A. Application of Fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions. Geoderma, 77, p , TAVARES FILHO, J.; EIRA, G.C.; FARINHA, L.R.L. Avaliação da compactação em um solo cultivado no sistema convencional (Compaction evaluation in a conventional tillage system). Engenharia Agrícola, Jaboticabal, Brasil, 19(2), , ZIMMERMANN, H.-J. Fuzzy Set Theory-and its applications. Boston, Kluwer Academic Publishers, p.435. FIGURE 1: Soil bulk density sampler designed by IAPAR. 100

5 FIGURE 2: Soil profile and penetration resistance evaluation. a) trench with wood frame for hand penetrometer evaluation, b) soil clod compacted by traffic. 1.5 observed soil density (g/cm3) R 2 = estimated soil density (g/cm3) FIGURE 3: Comparison of observed and estimated results of 5 cm depth model. 101

6 1.6 observed density (g/cm3) R 2 = estimated soil density (g/cm3) FIGURE 4: Comparison of observed and estimated results of 15 cm depth model. 102