2010 7th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE 2010) Tuxtla Gutiérrez, Chiapas, México. September 8-10, 2010.
Better Crop Management with Decision Support Systems Based on Wireless Sensor Networks Rolando A. Cárdenas Tamayo1, M. G. Lugo Ibarra1, J. Antonio García Macías1 1
Computer Science Department, CICESE Research Center, Ensenada, México Phone +52 (646)175-0500 ext. 23423 E-mail:
[email protected]
Abstract –– The agricultural sector constitutes one of the most important sources of income and production worldwide; its activities are directly related to issues such as water availability, soil conservation, pests and diseases. Therefore, it is important to have adequate control of environmental parameters and make efficient use of the resources through the implementation of constant monitoring systems. The environments of crops are highly dynamic; therefore systems that support the decision-taking process constitute a very valuable tool. We conducted the design and implementation of a decision-support system for monitoring crops using technologies such as wireless sensor networks. The prototype implemented includes tools that provide real-time information about the crop status, surrounding environment and potential risks such as pests and diseases. Moreover, we carried out an experimental evaluation based on the technology acceptance model (TAM) using the prototype with a group of potential users, as well as an evaluation of a predictive model for pests and diseases. This allowed us to gather their perception about usefulness, ease and intention of use, as well as the scope of the predictive model and its reliability. We believe that our proposal has the potential to reduce costs and using precise information, improve the management of resources for crop production. Keywords –– WSN, DSS, Agriculture, Management, Crops
We have structured our paper as follows: we will first give an overview of related work (section II), highlighting work in DSS for agriculture and the use of WSN as monitoring and control tools. In section III we will clearly identify the problems we are tackling and how our approach differs from others. Then, in section IV, we present the implementation details of our proposed solutions. The implemented systems are evaluated (section V) through experiments and the results obtained are shown. We conclude in section VI by presenting the kind of decisions that have been possible with our proposed solutions and outlining several lines for future work in order to have a more complete DSS. II. RELATED WORK Numerous studies exist on the management of crops, such as the work done by Wang et al. [1] where they describe several environmental parameters that influence crop protection as the key to greater automation and efficiency of them. Their main contribution is the design of hardware and software to solve problems related to the climate of greenhouses using WSN. They also implement methods to ensure data security and energy savings of the nodes.
I. INTRODUCTION Precision agriculture plays an important role in modern agriculture, as it may be used to more precisely evaluate optimum sowing density, estimate fertilizers and other inputs needs, and to more accurately predict crop yields. Precision agriculture relies on the continuous observation of several variables, including environmental (climatic change and control), economic (cost of resources such as water, soil, fertilizer, etc.), and human needs (product demand). In order to have continuous and precise observations, information technologies such as global positioning systems (GPS), sensors, satellites or aerial images, play a very important role.
Praxsoft [2] designed a DSS based on wireless sensor networks, where nodes are distributed throughout fields, across farms or other landscapes to gather real-time and long-term data such as soil moisture, fertilizer concentration, temperature and wind direction. The nodes then communicate this data through other nearby nodes in an adhoc fashion. This unique communications scheme allows the scattering of low-power nodes for optimal coverage, eliminating expensive communications costs. The nodes find the best path back to the base station through one or more collection points. Several long-range legacy communication methods, including satellite, radio telemetry and cellular links, provide reach-back from the collection point.
This paper reports our work toward building a decisionsupport system (DSS) aimed at supported processes such as fertigation, crop growth control and prediction of diseases. During the observation phase, data is recollected with wireless sensor networks (WSN) and then the information gathered is analyzed and serves as input to different decision-taking processes and algorithms.
IEEE Catalog Number: CFP10827-ART ISBN: 978-1-4244-7314-4 978-1-4244-7314-4/10/$26.00 ©2010 IEEE
Bencini [3] in his work designed a system that implements and communicates several wireless nodes, that send environmental data every 15 minutes to a master node connected to a GPRS gateway board that forwards data to a remote server using standard TCP/IP protocols. This system has gathered a great amount of environmental data from
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2010 7th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE 2010) Tuxtla Gutiérrez, Chiapas, México. September 8-10, 2010. several pilot sites in different vineyard in Italy and France. The data collected and analyzed has been used for pathogens control for two and half years.
Controlling the growth of a crop is a complex process. Given that the final product of a crop is but the consequence of the agricultural tasks performed during a full cycle, an adequate control of it involves knowledge of agricultural phenology and the possible duration of the different stages of growth in crops [4].
Currently, there are several systems for monitoring crops. However, the vast majority propose custom-made, ad-hoc solutions. All these solutions have in common a dependency between the hardware and software provided. For this reason, it is impossible to provide new hardware support and extend the functionality of the software tools. It is therefore very complex to replicate the results obtained by these systems in regions with different climatic and environmental conditions.
B. Fertigation Fertigation can be seen as the application of nutrient solutions needed for the growth of plants [5]. It involves the management of all those mineral and organic elements, as well as water, required by plants according to their physiological needs, related to: their phenological stage, chemical composition and period of application [6]. The basic principle for growth control in crops is to monitor the mineral elements in water, soil and plants [7]. For this purpose, it is necessary the utilization of a series of analyses and nutrition techniques such as soil analysis, water analysis, and analysis of soil solution, among others. It is required to establish adequate standards and optimal concentrations for each type of analysis, crop and phenological stage, where the intervention of specialized equipment is needed for calibration and joint interpretation of the different types of analyses. The basic purpose of crop analysis is the prediction of future nutrimental problems, monitoring to know the nutrimental dynamics, and diagnostics to evaluate the possible causes of a restricted physiological development due to some deficiency, excess or problem of nutrimental nature.
Another problem found in crop monitoring systems is their inability to adapt to the constantly changing needs of the plants, because these needs vary in each of the phenological stages. For this reason, it is necessary to have crop monitoring systems that provide flexibility to extend its functionality and therefore will be ready for the demanding and dynamic needs of the agricultural sector. With the above information, we propose a series of requirements that must be considered when designing monitoring systems to support decision-taking in crop management, these requirements are as follows: • • • •
Allow access to one or more monitoring networks. Allow the integration of new hardware for monitoring. Allow the integration of new software functionality. Provide multi-platform support.
C. Pests and Diseases
Thus, this paper proposes the use of a software tool, based on wireless sensor networks, for improving environmental quality, better management of scarce resources (e.g., water), maintaining an increased long-term production and prepare agricultural production systems for future conditions and requirements.
Due to the great variability in climatological conditions where crops are produced, there are many cases in which the right conditions are given for the development of pests and diseases. Besides, the study of the factors causing these, including climatological, technological and phytopathological factors, is very complex; even more if other variables such as water and soil are considered. Thus, the absence of predictive models based on climatological and phenological parameters makes very difficult the prediction based on software tools that up to now have focused on particular crops or regions.
III. THE CHALLENGES WITH STUDYING CROP MANAGEMENT The study of factors involved during agricultural production processes represents a wide area of opportunity, as there are many parameters involved during the growth cycle of a crop, which vary according to phenological stages. It is extremely important to have an adequate control of these parameters in order to minimize the irrational consumption of resources, as well as to prevent significative losses due to the presence of pests and diseases. Thus, controlling the relevant parameters will yield better growth in crops. In the following subsections we discuss the factors that, according to our proposal, should be taken into account in order to appropriately carry out the processes of fertigation and prediction of pests and diseases in crops.
IV. SYSTEM IMPLEMENTATION In this section we propose a system for monitoring and prediction of crop pests and diseases. The system was developed based on the Java language, using the JPF framework1 to provide an infrastructure with great modularity and extensibility capabilities. It additionally provides the functionality to dynamically discover and load new software modules (plugins) during execution time.
A. Crop Growth
IEEE Catalog Number: CFP10827-ART ISBN: 978-1-4244-7314-4 978-1-4244-7314-4/10/$26.00 ©2010 IEEE
1
http://jpf.sourceforge.net
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2010 7th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE 2010) Tuxtla Gutiérrez, Chiapas, México. September 8-10, 2010. External communications are handled by the system via the ActiveCloud middleware [8], which provides communication between hardware components and our system. These communications are made by means of software components called capsules, providing a series of high-level communication services to interact with heterogeneous hardware. Figure 1 presents the system architecture.
Fig. 2. System Screenshots
B. Prediction of Pests and Diseases in Crops The predictive model for pests and diseases has been designed to be the base structure for a more robust model, specifying the basic parameters such as phenological stage of the crop, climatological conditions for the environment and the soil, thresholds for pest growth, diseases and crops. The phenological stages constitute a crucial parameter for the predictive model, because the different climatic factors essential for crop development are involved [13].
Fig. 1. System Architecture
Some of the functionalities implemented by the system are: prediction taking about the time of growth for crops, aid in fertigation, and predictions related to pests and diseases. We will discuss these functionalities next:
Several tools and methods were considered for the predictive model for pests and diseases, such as neural networks and evolutionary algorithms. Algorithms were evaluated based on their precision, simplicity and interoperability [14, 15]. We also evaluated KEEL [16], a software tool to assess evolutionary algorithms for Data Mining problems including regression, classification, clustering, pattern mining and so on; we used this software tool with data obtained through our sensor networks. However, we used the backpropagation neural networks technique due to its flexibility and how well it fit the problems at hand. Analogously, Linker [17] proposes a predictive model using neural networks for protected agriculture.
A. Crops Growth and Fertigation The estimation of crop growth was based on the concept known as degree-days, which can be defined as the amount of heat necessary for a plant to go from a stage to the next in its growth cycle [9]. Degree-days constitute an important solution as they present characteristics that can be adapted depending on the type of crop, region and season of the year. Additionally, it is necessary to take into account historical records about temperatures in the region. The importance in the estimation of crops growth resides in the wide array of possibilities presented to the crop managers when taking decisions related to the proper management of different plants.
C. Determining Factors Once the basic parameters for climate control (CC) and phenological stages (PS) were obtained, we classified pests and diseases in order to form groups to initiate the structure of the neural network. Similar conditions were found for some pests (insects, acaroids, and nematodes) and diseases (bacterial, fungal, viral), which are shown in three groups. Insects and viral diseases tend to develop in extreme climates, acaroids and bacterial diseases develop in fresh climates, while nematodes and fungal diseases act during mild climates (see Table 1).
Another task supported by our system is fertigation, which can be defined as the application to a crop of nutrient solutions necessary for the adequate growth of plants [10]. For its implementation it was necessary to take into account the amount, proportion and chemical form of the solutions required by the plants according to their phenological stage, growth rhythm, and accumulation of dry matter [11] all these with the objective of achieving, at short and long terms, high yields in production and an increase in the quality of the products [12]. The fertigation module calculates the amount of fertilizer needed to add to a water solution in sufficient amount to irrigate the crop. This calculation takes into account the water and soil analyses, as well as the type of crop. It is important that the information from the analyses be recent (at most from the last planting season) in order to have an adequate accuracy. Figure 2 presents some screen captures of the system.
IEEE Catalog Number: CFP10827-ART ISBN: 978-1-4244-7314-4 978-1-4244-7314-4/10/$26.00 ©2010 IEEE
TABLE I CLASSIFICATION OF GROUPS OF PEST AND DISEASES
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2010 7th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE 2010) Tuxtla Gutiérrez, Chiapas, México. September 8-10, 2010. The following variables were considered for climate control of the environment and soil: maximum and minimum ambient temperature (TAM, TAm), maximum and minimum soil temperature (TSM, TSm), as well as humidity. Data obtained from the WSN was sampled at intervals of 30 minutes with 48 daily possible incidences. Derived from the field observations, it is estimated that if there are 16 incidences (humidity or temperature parameters out of range for normal plant growth) during the day, then it is considered as an alert incidence. If these are presented during 3 days, even in non-consecutive, an alert is generated.
used as a measure to determine the use of the final system, so its result is to provide interesting and shows the importance for user of the system, the implementation of the processes of fertigation and control the growth of a culture.
Using a 4-variable Karnaugh map (for the example of ambient temperatures), possible alert incidences was located because there were values above or below the optimal development thresholds for the crop (rows 6 and 9). Likewise, values were located where conditions were for normal conditions within the thresholds (rows 5 and 10), as shown in Figure 3.
TABLE 2 EVALUATION OF TECHNOLOGY ACCEPTANCE
The results obtained by the evaluation show that average was obtained in 3.962 for the intended use of the developed system, since this value is very close to 4 (on a scale of 1-5 used for this assessment) can be said that a group significant of people tested, consider using the system for support in carrying out their duties. The Table 2 shows the percentages obtained for the tested concepts:
For the evaluation of the predictive model, the behavior of the crop during a period of six months was observed. Data recollection (temperature and humidity for the environment and the soil) was made using Eko Pro Series [19] sensing nodes, forming a wireless sensors network shown in Figure 4. The experiments were carried out in a farm located in Valle de Guadalupe, in the state of Baja California (northwestern Mexico). Two types of crops were selected (tomatoes in a greenhouse and in the open field and zucchini in a greenhouse) for monitoring since the first phenological stage. It is worth noting that an agronomist carried out an external monitoring process, so the data available through this alternative and more traditional process was available to contrast with the method using WSN. For our evaluation, the percentage of predictions that turned out to be right, made by the system, was counted.
Fig. 3. Recognition of input values, incident and alert thresholds
The structure of the neural network was formed as follows: groups A (row 5) and D (row 10) represents values from 0.0 to 0.2 and 0.9 to 1.0, respectively. Group B (row 6) and group C (row 9) represent values from 0.3 to 0.5 and 0.6 to 0.8 respectively. Refer to Figure 3 for these values. In this manner, the structure of the network is formalized as follows. Inputs: TAM, TAm, HRAM, HRAm (remaining CC values); outputs: represented by values in Figure 3. Type of network: backpropagation. Architecture: 4,n,4. To set the number of layers several trainings were performed, varying the number of hidden layers, until determining the least quadratic error. V. EXPERIMENTS AND EVALUATION In order to obtain information to evaluate the impact of information provided by the system as potential users, we carried out a questionnaire using a Likert scale [11] to assess three different factors, which are based on the TAM [12] model. Through these factors was possible to measure the level of general acceptance of developed system, with respect to its ease of use, usefulness and intention to use. Intention to use a technology is determined by the individual's attitude toward the use of such technology [18] In turn, the attitude is determined by the perceived usefulness and perceived ease of use. These factors were
IEEE Catalog Number: CFP10827-ART ISBN: 978-1-4244-7314-4 978-1-4244-7314-4/10/$26.00 ©2010 IEEE
Fig. 4. Wireless Sensor Network deployment
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2010 7th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE 2010) Tuxtla Gutiérrez, Chiapas, México. September 8-10, 2010. The experimental statistical analysis was performed by comparing the boxplot and ANOVA (analysis of variance) diagrams, which were collated against the results obtained by the base predictive model (BPM). The boxplots allow the clear and individual identification of the variability in the behavior of the analyzed values and the sources for this variability, also allowing identifying the observed significance against the predefined significance [20], in this case the one proposed by the BPM. Additionally the analysis of variance (ANOVA) identifies and separates the total variation of the analyzed factors in the study [21]. For this study both analyses were performed in order to provide significative proof that evidences the functionality of the BPM. Fig. 7. Prediction of pest (tomato-Open field), by MPB
We now describe the results obtained through the statistical analysis, which was performed using the MINITAB statistical software. In general, for the treatment of data a significance level (α) of 0.05 was used. The results of the ANOVA, in all cases (Figures: 5, 6 y 7), show that the p-value is less than the preset significance, i.e., it effectively exists a difference between the four predictive values of the BPM [21].
Likewise, it can be observed that for all cases the higher variability is found in the Upcoming and Null factors, which coincides with that proposed by Linker [17], who additionally considered outside temperature and humidity for a greenhouse. The boxplots show the dispersion that exists between the factors of the study and the period of observations made during September 2007 to January 2008 and February to August 2008, for tomatoes in open field and greenhouse, respectively; also for February to August 2009 for pumpkin zucchini in the greenhouse. In general, the diagrams show that the period of study affects significatively (α= 0.05) the behavior of the studied factors. Nonetheless, it is observed that in most cases plagues and diseases predicted by the PBM and those observed (real ones), present an statistically insignificant difference, according the performed analyses. For the particular case of the farm where the experiments were carried out, the information provided by the sensor network was also useful for determining that if the planting stage for tomatoes during autumn is delayed by two months, then the risk of damages due to climatological phenomena is significantly reduced. Additionally, the temperature map for the greenhouses aided in determining that a rotation of 90 degrees would allow for better ventilation, and thus better control of the temperature inside; this will be considered for future greenhouse installations.
Fig. 5. Prediction of Pest (tomato-greenhouse), by MPB
V. CONCLUSIONS Wireless sensor networks (WSN) technology gives crops managers the possibility of accurately measuring environmental aspects affecting agricultural production. Having this basic environmental information helps prevent damage to crops and pest attacks and diseases. The results presented by our proposed system show different aspects about a particular crop; the information provided by the monitoring system provides the crops managers with valuable tools for supporting their decision-taking processes.
Fig. 6. Prediction of diseases (pumpkin-greenhouse), by MPB
IEEE Catalog Number: CFP10827-ART ISBN: 978-1-4244-7314-4 978-1-4244-7314-4/10/$26.00 ©2010 IEEE
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2010 7th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE 2010) Tuxtla Gutiérrez, Chiapas, México. September 8-10, 2010. [4] T. Hodges, P. Doraiswamy, “Crop phenology literature review for corn, soybean, wheat, barley, sorghum, rice, cotton and sunflower”, Terra, vol. 17, no. 3, p. 30, 1979.
Based on the results obtained in this work and the statistical analysis, we can conclude that the BPM is capable of predicting plagues and diseases for tomato and pumpkin zucchini crops in open field and in greenhouses. However, it is important to consider that the parameters used for the BPM (phenological stages and climatological conditions) could be considered in future research work for the prediction of plagues and diseases in several types of crops.
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One of the main contributions of the presented work is the proposal of flexible models to make predictions about crops growth, pests and diseases in an open field or greenhouse. Based on the analysis and the reach of this work, it is suggested:
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b) To include metrics for the evaluation of parameters related to the behavior thresholds during the different phenological stages. c) To utilize an experimental design methodology that allows increasing efficiency, economy and scientific objectivity for the resources to be used in future studies. There is definitely much work to be done. Specifically, it is necessary that the system supports all types of currently used crop production methods (hydroponics, aquaponics, etc.). However, we believe that our proposal has the potential to provide a useful tool to reduce crop production costs and environmental impact through the use of accurate information.
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ACKNOWLEDGMENT Financial support for this project was provided by the Mexican Council for Science and Technology (CONACYT). REFERENCES [1] C. Wang, C. Zhao, X. Qiao, X. Zhang, and Y. Zhang, “The desing of wireless sensor networks node for measuring the greenhouses environment parameters,” Computer and Computing Technologies in Agriculture, vol. 2, pp. 1037–1045, 2008. [2] I. Praxsoft. (2010) Praxsoft. [Online]. Available: http://www. praxsoft.com/sol agriculture.html [3] L. Bencini, F. Chiti, G. Collodi, D. D. Palma, R. Fantacci, A. Manes, and G. Manes, “Agricultural monitoring based on wireless sensor network technology: Real long life deployments for physiology and pathogens control,” in Third International Conference on Sensor Technologies and Applications Sensorcomm’09, Athens/Glyfada, Greece, Jun. 18–23, 2009, pp. 372–377.
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