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Author(s)

First Name Middle Name Surname Role Email

Ying Zang N.A. [email protected]

Affiliation

Organization Address Country

South China Agricultural University of China

Guangzhou, Guangdong Province

P.R.China

Author(s) – repeat Author and Affiliation boxes as needed--


Xiwen Luo Member of ASABE

[email protected]

Affiliation




P.R.China


Zhiyan Zhou N.A. [email protected]

The authors are solely responsible for the content of this technical presentation. The technical presentation does not necessarily reflect the official position of the American Society of Agricultural and Biological Engineers (ASABE), and its printing and distribution does not constitute an endorsement of views which may be expressed. Technical presentations are not subject to the formal peer review process by ASABE editorial committees; therefore, they are not to be presented as refereed publications. Citation of this work should state that it is from an ASABE meeting paper. EXAMPLE: Author's Last Name, Initials. 2007. Title of Presentation. ASABE Paper No. 073120. St. Joseph, Mich.: ASABE. For information about securing permission to reprint or reproduce a technical presentation, please contact ASABE at [email protected] or 269-429-0300 (2950 Niles Road, St. Joseph, MI 49085-9659 USA).

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Publication Information

Pub ID Pub Date

073120 2007 ASABE Annual Meeting Paper



An ASABE Meeting Presentation

Paper Number: 073120

Study on farming information acquisition technique for precision agriculture

Ying Zang, PhD. College of Engineering, South China Agricultural University. Guangzhou, P.R China

[email protected].

Xiwen Luo, Professor College of Engineering, South China Agricultural University. Guangzhou, P.R China

[email protected].

Zhiyan Zhou, Research AssociateCollege of Engineering, South China Agricultural University. Guangzhou, P.R China

[email protected].

Written for presentation at the2007 ASABE Annual International Meeting

Sponsored by ASABEMinneapolis Convention Center

Minneapolis, Minnesota17 - 20 June 2007

Abstract. With the progress and application of information technology in agriculture, precision agriculture (PA) has been increasingly gaining attentions in worldwide. PA technology is a kind of agricultural management system on the basis of the variability of spatial and temporal of crop yield of small section and circumstance factor in the field. To measure timely and efficiently the spatial and temporal variant information that influences crop production is the key step for implementing PA. The current status of the acquisition parameter techniques of soil variability, the distribution of crop yield, diseases and insect pests and crop growth condition was analyzed, which showed that most of research focused on the acquisition and processing techniques of soil variability, the acquisition technique mostly didn’t realize the quick and real-time collection, and the single functional instrument was used. The development and research on the yield monitor system, the instrument for collecting timely soil parameter variability and integrating multifunctional information collection system will be the direction carried out in the future in China.

Keywords. Precision agriculture, agricultural condition information, information acquisition

The authors are solely responsible for the content of this technical presentation. The technical presentation does not necessarily reflect the official position of the American Society of Agricultural and Biological Engineers (ASABE), and its printing and distribution does not constitute an endorsement of views which may be expressed. Technical presentations are not subject to the formal peer review process by ASABE editorial committees; therefore, they are not to be presented as refereed publications. Citation of this work should state that it is from an ASABE meeting paper. EXAMPLE: Author's Last Name, Initials. 2007. Title of Presentation. ASABE Paper No. 07xxxx. St. Joseph, Mich.: ASABE. For information about securing permission to reprint or reproduce a technical presentation, please contact ASABE at [email protected] or 269-429-0300 (2950 Niles Road, St. Joseph, MI 49085-9659 USA).

IntroductionThe precision agriculture (PA), initiated in the mid 1980s, is an emerging interdisciplinary technology by which better field investigation methods including soil survey, soil sampling, crop scouting et al., resulted in a better awareness of soil and crop conditions variability within fields. A significant outcome was to estimate potential benefit of crop management by blocks within fields for increased profitability and environmental protection.

Information is perhaps the modern farmer's most valuable resource. Timely and accurate information is essential in all phases of production from planning through post-harvest for PA. Conventional information monitor technique has not reached the demand of modern PA. Therefore it is urgent to develop rapid information monitor technique for the PA. The progress of the information monitor technique for the soil environment, plant growth response, weed and insect populations, grain yield, etc, as well as further suggestion, will be introduced in this article.

Information acquisition for soil environment information Nowadays the research content for PA is started from the spatial variability of soil prosperity, and mainly focus on rapid information monitor including the soil nutrition (N, P, K, SOM et al.), soil moisture, the electrical conductivity, the pH value, the cultivation resistance (circular cone index) and any other essential factors.

Soil nutrition monitoring

It is the key step for PA monitor technology to rapidly monitor the soil nutrient. Three types of instrument were employed for monitoring soil nutrition. Based on the traditional photoelectricity assay method, Henan Agriculture University developed the portable instrument with the 5-10 % error, which can get the value of soil fertilizer in the 40-50 min as described by Hu et al. (2002).

Second type is based on Near Infrared (NIR) technique and can get the solid fertilizer value by the soil or leaf surface reflection spectral characteristics. For example, monitoring the refection spectrum between 1603-2598 nm from soil by NIR sensor can be used to predict the soil organic component and water content, with the 0.62% and 5.31% error, respectively.

Last type is based on ISFET technique, and can quickly monitor soil mineral component. ISFET/FIA soil analysis system can test the concentration of sodium nitrate in soil solution in 1.25 second, which reaches the requirement for the real-time analysis in the field as described by Birrell et al. (2001).

Soil moisture monitoring

Soil moisture is the soil important constituent, and soil moisture monitoring is the basic part for water saving and irrigation. The research and development of soil moisture sensor technology directly determine the progress of the PA irrigation technology.

Presently, the most popular monitoring method is based on the time domain reflectometry (TDR) principle. Such as SWR serial monitor coupled with GPS and GIS as described earlier (Sun et al., 1999 and Qiu et al., 2003). Another popular monitor is Neutron Soil Moisture monitor, which can measure soil moisture content in the various depths, but keep the soil intact. Other methods, based on the soil moisture tension force and electromagnetic wave principles, were also developed for monitoring soil moisture content.

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Soil electrical conductivity (EC) monitoring

The indicator of soil EC can be used to calculate the soil salinity, water content, organic content, and soil texture structure and porosity sizes. It is important to get EC for identification the parameters distribution within field in the time and spatial variability.

Carter et al. developed a car-carrying monitor based on current-voltage four-electrode method, which can work only in the large field before planting season as described by Carter et al.(1993). Li et al. developed a portable detector based on four-electrode measurement method. The detector includes the sensor, the control and data acquisition box, as well as the data processing software. It can monitor soil EC whenever resting, planting, growing or harvesting seasons as described by Qi et al. (2003) and Li et al. (2004). Other methods based on electromagnetic interference or TDR principles were also used to monitor soil EC as described earlier (Carter et al., 1993 and Myers et al., 2000).

Soil pH value monitoring

The pH paper or pH calibrator are widely used to monitor soil pH value, however these methods can not reach the requirement for collection technology of PA for their limitation. The present popular instruments are photo-fabric pH value sensor and pH-ISFET electrode. The former in the aspect of precision and response time can reach the requirement for collection in the field, although suffered from environmental light. The latter has well precision and short response time but is subject from environment temperature.

Soil tillage resistance monitoring Cone index (CI), as a means of assessing soil mechanical properties, indicates the depth of soil tillage layer and tillage resistance. Penetrometers are typically used to measure CI, and then identify soil condition. Commonly used penetrometers include the pocket, cone, and small−diameter friction sleeve cone types as described by Lowery and Morrison (2002). Although the processing of sampling, analysis and recording is implemented automatically, most penetrometers need to be penetrated by human. Presently, the spatial variability in soil mechanical strength was visualized through layers of maps in Differential Global Positioning System (DGPS), GIS & Decision Support System (DSS) as described by Luo et al. (1996).

Information acquisition for crop yield distribution Obtaining crop yield data and generating yield map are very important parts for PA (Lars Thylen, 1996). Presently, the oversea commercial product of grain yield monitoring system is focused on grain harvester, such as Advanced Farming System (AFS) made by American CASE IH Corporation, Fieldstar system developed by English AGCO Corporation, American John Deree Corporation's Greenstar system, etc. They all have strong GIS synthesis function, and could automatically monitor the grain yield and generate yield map. However, the study on the grain yield monitoring system is underway in China, and commercial products are not available in this area as described by Zhang (2003).

Grain yield sensor is the core part for the whole yield monitoring system, and can demonstrate well the value of site-specific information and the potential for site-specific management. Presently, the impact flux sensor, γ radial flux sensor and photoelectricity flux sensor are widely used in the PA. Grain moisture sensors are another essential part for yield monitoring system and have many classes, such as resistance sensor, capacitance sensor, infrared sensor, microwave sensor and neutron sensor.

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Recognization of weed, plant diseases and insect pests

Recognization of weed Yonekawa et al. (1996) and Franz et al. (1991) adopted the shape characteristic analytic method to recognize the weed, according to some shape factors of plant, such as tightness, roundness, elongation, leaf-shape, roughness, and bending ratio of leaf edge, etc. They have well discrimination ability for weed from crop, and can reach the requirement for online real-time recognition in the field.

The low recognition rate is the big problem for recognization of weed because leaves cover each other. Burks (2000), Tang (1997) and Wang (2001) et al. developed the spectrum characteristic analytic method based on the difference in the leaves spectrum reflection. This method has high efficiency and speed for discrimination between crop and weed, and reaches the requirement for the field online real-time recognition. But the results still are affected by many factors and the high cost needed for the more accessories to get the high resolution.

Ji et al. (2001) developed a method that can recognize the field weeds from the corn seedling, according to characteristic index, such as projective area, leaf length and width etc. The method could get rid of soil background and identify acerose endogen weed correctly, although the method is affected by illumination. Wang et al. (2004) studied on the real-time weed recognition system, which includes the image getting module, the pre-process module, the partition of plant from background module, and the partition of weed module. The last weed image can be used to count the density of weed, also the detection of weed in real-time is feasible.

Recognization of plant diseases and insect pests The technology based on SOM and the multi-layered sensation neural network was used to recognize the verticillium wilt of the wheat leaf. Result showed that the classified precision reach 99 % as described by Birrell (2001), but the real time of this recognition algorithm needs to be confirmed by the further study.

Chen et al. (2001) studied on automatic measurement of danger degree of cotton insect pests using computer vision, based on the inner hole and irregular edge of cotton leaves. The results showed that the method can effectively judge the danger degree of insect pests of cotton, and the measurement error of danger degree was less than 0.05.

According to the properties of plant disease image, Tian et al. (2004) adopted the statistical pattern recognition to supervise and sort crop disease by using color as characteristic space. Based on the features of color texture image of plant disease, the support vector machine (SVM) and chromaticity moment were used for recognition of plant diseases. The experimental results proved that chromaticity moment is simple, efficient, and effective for recognition of plant disease image. Furthermore, the SVM method has excellent classification and generalization ability in solving learning problem with small training set of sample, and is fit for classification of plant disease.

Ma et al. (2004) applied Mathematical Morphology to plant diseases and insect pests recognition. The operations of dilation and erosion by structuring element are implemented by a fast algorithm. According to the quadrature feature of different pest skeletons, they proposed a new insect pests recognition method with combination of Mathematical Morphology and Neural Networks. The experiment results have shown that Mathematical Morphology has good performance; however this method could not recognize the degree of insect pests.

Zhang et al. (2003) used multi spectrum RS image to detect the diseases and insect pests of tomato. The experimental results showed that near infrared band (0.7-1.3µm) is more significant

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than visible light band in monitoring the crop diseases and insect pests. It is marked for 0.7-0.9µm band to inspect the tomato late blight. But the further test needs to be performed in the fields.

Information acquisition for crops growing trend It is important for the crops growing trend information to regulate the crops growing, estimate the lack of crops nutrition, analyze and predict crops production. From the macroscopic angle, RS is employed to inspect the growth rhythm characteristic (Sha et al., 2003). Now many researches mainly focus on the crown level biochemistry parameter derived from the spectrum information inversion, even though several groups stepped in the high spectrum remote sensing in the vegetation biophysics and the biochemistry information extraction aspect research as described by Zhao et al. (2003).

Rhizome

The traditional measurement methods of the rhizome have many kinds. Such as nail board method, vessel measuring method, radioactivity monitoring method, underground root room method, and analytical method by scanning the sample based on computer visual technology

Luo et al. (2004) put forward the visualization of plant root morphology in situ based on X-ray CT imaging technology, which could observe and measure the plant root system in situ, correctly, quickly and non-destructively.

Plant height

Searcy et al. (2000) developed an instrument, which can real-time assess cotton plant height with the infrared sensor installing the manipulator. Gao et al. (2004) used 40KHZ ultrasonic sensor to measure the plant height, which could be used on the automatic adjusting system of many agricultural machine, automatic picking machine hand, and controlling system of agricultural robot. However, this equipment can not real-time batch acquires data in the field because of the great measuring error.

Plant physiology information Presently, the inspection of plant physiology signal was implemented by micro-electrode sensor for plant potential signal, by micro-electrode sensor and constant-current source technology for resistance signal. Furthermore, the signal of plant transpiration quantity and velocity was can be collected by micro-weight sensor, and the signal of the velocity photosynthesis by infrared sensor as described by Bai et al. (1995).

Crop water stress index (CWSI）Wang et al. developed vegetables drought situation evaluation system for examining the field moisture situation. The relationship between canopy-air temperature difference and vapor pressure deficit was determined by the air temperature and humidity, bulb temperature, and soil moisture content measured by an infrared thermometer. And then the preliminary model was established with regard to the influence of radiation intensity. A microcomputerized real-time data acquisition system was developed to monitor water-stress status for scheduling irrigation of vegetables.

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Nutrition deficit stress Anom et al. (2000) developed the real-time assay spectrophotometer, which could online measure the water, disease, salinity and the distribution and nutrition deficit stress index of crop, etc. Ahmad et al. (2000) studied the N stress distribution of maize using chlorophyll monitoring instrument based on DGPS.

Li (2001) studied the two dimensional reflectometry of rice canopy using the Fieldspec® spectrum of Analytical Spectral Device company, which can calculated the change law of canopy two dimensional reflectance with different nitrogen and integrated the plant spectrum simulation system. In this system, the canopy structure and leaf biochemical components can be derived by imported parameters such as canopy two dimensional reflectance, elliptical model parameters, view geometry and view time, which is very significant for monitoring rice growth and forecasting rice yield.

Wang et al. (2002) studied the relationship between N level of rice and spectrum character. By measuring the spectrum, the various form of N level of rice could be calculated. Zhao et al. (2004) developed the novel normalized difference vegetation index instrument (NDVI). The NDVI is derived from the incident and reflected radiance of vegetation at the red and near infrared bands by the four interference filters. The winter wheat's leaf area index and chlorophyll density are successfully calculated by the NDVI instrument which demonstrates the promising application of the NDVI instrument.

System for farming information acquisition Now the commercial software and hardware product about farming information acquisition are available in many countries. Software has many functions; especially the technology in graph manipulation and visual analysis is very good. Hardware can reach the requirements of collecting as described by Liu et al. (2002).The information acquisition software based on palm-size PC and developed by Field Worker corporation can real-time collect crop growing information with spatial character in field and carry on corresponding computation processing. This provides the scientific basis for the crops management.

Research on the farming information acquisition system based on GPS and GIS in China was initiated lately, and most of them are being at the manual gathering stage. At present, some scientific research departments in China positively begin to develop farming information acquisition system suitable for Chinese condition.

i. The farming information acquisition system, the core of which is a single chip, can collect soil moisture, nutrient, conductivity and other information. And then the gathered information will be managed and analyzed. This kind of equipment has simple function, and the development cost was low.

ii. The farming information acquisition system based on palm-size PC can quickly gather a lot of farming information on spot and analyze the information visually. Presently, this software is developed as described by Liu et al. (2002).

iii. The embedded collecting system of agricultural information based on palm-size PC was developed. Rao et al. (2002) brought forward the scheme of combining palm-size PC with GPS and developed the GPS data collector based on palm-size PC. This equipment has the function such as collecting and handling data, location, and navigation and saving etc. But the speed of CPU, capacity of saving and operation system had an effect on the development of the application.

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ConclusionRecently, the researches of farming information acquisition technology are concentrated in soil environmental information gathering and processing. Most of farming information acquisition technologies did not realize real-time automatic acquisition. Moreover, most of acquisition equipments had simple function.

Based on the past research on farming information acquisition technology, more attentions should be pay to increase the accuracy and speed of farming informing acquisition equipment, and develop more multifunction equipment so as to enhance the efficiency of data collecting and low cost for data collecting and grain yield monitoring system for combine harvester, and so on.

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Paper No: 200000mohtar/IET2007/073120.doc · Web viewIf you can't use this Word document and you'd like a PDF cover sheet please contact Sandy. Please have Word's AutoFormat features