Farming Decision Support by ICT Presentation at Agriculture WG The - - PowerPoint PPT Presentation

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Farming Decision Support by ICT

Presentation at Agriculture WG

The 30th APAN Meeting, 9-13, August 2010, Melia Hotel, Hanoi, Vietnam NANSEKI, Teruaki

Professor, Kyushu University, Japan nanseki@agr.kyushu-u.ac.jp

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Speech outline

• 1. Introduction

– The follows two examples are given as examples of ICT application for farming – 1.Appropriate agro-chemical use – 2. Succession of skilled farming operation

• 2. Support of appropriate agro-chemical

use by ICT

– Agro-chemical regulations in Japan – Warning system for illegal agro-chemical use

• 3. Succession of farming skill by ICT

– Learning from skilled operator – Recording farming by skilled operator with ICT – FVS: Farming visualization system for personnel training

• 4. Concluding remarks

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• 2. Support of appropriate agro-chemical use by ICT
• Appropriate Agro-chemical use is

– One of the GAP objectives – Crucial for food safety and environment as well as security (health) of farming operator

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Agro-chemicals Scandal and Agro-chemicals Law in Japan

• Agro-chemicals Scandal of unregistered agro-chemicals

happened in 2002 in Japan.

– At that time, Japanese consumer felt that farm products were not safe.

• This acandals became to be a big political issue and

Agro-chemicals Law was revised 2003.

– The revised Agro-chemicals Law has very heavy penalty on an illegal use of agro-chemicals. – three years penal servitude – one million yen (10000 US$) amercement – Any farm products with illegal use of agro-chemicals can not be sold and it is abandoned.

• The standards for agro-chemical use is

– very strict, detailed and complicated

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Pesticide regulations in Japan

Name of Law Agricultural Chemical Regular Law Penal provision (Punishment) three years penal servitude

• ne million yen (10000 US$)

amercement Identification and management system of pesticide product Registry number by Ministry of Agriculture, Forestry and Fisheries The registry number is printed on each commercial container. Number of registered pesticide product 4277 in Total 1214(Insecticide), 1454(Sterilizer) 1609(Herbicide) Number of registered chemicals active ingredient 477

We identify and control agrochemical by the registry number

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The registry number and control standards of agro-chemical : A Example

• Name, Registered number
• Name of chemicals active ingredient
• Percentage content of chemicals active ingredient
• Target crop
• Target pathogenic bacteria
• Method
• Amount
• Dilution
• Maximum frequency of use
• (crops season)
• Min. pre-harvest period for pesticide use
• Maximum frequency of chemicals active ingredient

The label of agro-chemical should be attached on the container by the law.

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Example of Control standards by Agro-chemical Law : Daconil, Japan

Name (registered number) Daconel granule wettable powder (No.20168) Name of chemicals active ingredient Chlorothalonil (same to TNP) Percentage content

• f

chemicals active ingredient TNP 82.5% powder Target crop Tomato Target pathogenic bacteria Epidemic Method Spraying Amount No description. Dilution 1500 dilution Maximum frequency of use (crops season) 4 times

• Min. pre-harvest period for pesticide

use The day before harvest. Maximum frequency

• f

chemicals active ingredient (Chlorothalonil) 4 times (The soil douche is within two times.) MRLs (Maximum Residue Limits) 5ppm

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Noyaku-Navi :Warning system for illegal agro-chemical use

• The goal of the system is

– to enable farmers to prevent carelessness in agricultural chemical misapplication. – to register the application history easily and correctly

• Two types of System are developed

– Mobile phone-based system

• Scan bar code by camera-mobile phone
• Convert the code to the registered number

– OCR-based system

• Hand write the date with pre-printed format sheet
• Convert the date to the digital data

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Experimental running of the system in Yamagata Prefecture : Optical character recognition (OCR) vs. mobile phone

620 farmers used OCR-based system 30 farmers used mobile phone-based system OCR: Optical Character Recognition Experimental use of the system with agricultural cooperative

10 The No Nouy uyaku- ku-na navi vi judgmen judgment serve server The No Nouy uyaku- ku-na navi vi judgmen judgment serve server

1.

• 1. Prepare a

Prepare a pe pesticide sticide Sprayi Spraying pl ng plan an 2. 2. Re Request a quest a judgment for the judgment for the plan plan 3. 3. You get You get resul results of s of judgment judgment

• Total frequency of

Total frequency of use of th use of the active e active ing ingredient will edient will exceed exceed th the total e total frequ frequency prescribed ncy prescribed in the law. in the law.

• Illegal
• Note
• Memo

Noyaku-Navi System : Mobile phone-based system

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Mobile Phones System (cont.)

:Web applications system on mobile phone

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when Who Where Why How What Where

Mobile Phones System (cont.)

5W1H information of agrochemicals use is able to be obtained and managed by camera-equipped GPS mobile phone.

The 5W1H historical information on agrochemical application automatically recorded. When : time of access to the server Where : name of farm fields or latitude and longitude obtained by the GPS Who :name of producer, login ID, or identification information of the mobile phone What :registration number or name of the agrochemical, Why :images of target agricultural pest and weeds How :dilution rate, amount, or images of spray may be also available

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Evaluation of mobile phone-based approach

Age

want to use the mobile phone system continuously Dose not want to use the mobile phone system continuously

Over 51 27%

73%

Under 50

69%

31% Total (100%) 50% 50%

Note: n=24

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Impacts of the system

• Before introduction of the OCR-based

system

– 31 hours (3days by 4 person) for checking the hand write agrochemical history

• After introduction of the OCR-based system

– 15 minutes for checking the agrochemical history – More precise checking

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Mobile Phone vs. OCR

• OCR-based system

– Advantage

• easy installation and extension
• 16000 farmers use this system in Japan

– Disadvantage:

• difficult to actually collect and judge the documents often,
• prejudgment just before spraying cannot be performed,
• reading and correction of OCR documents at the JA branch
• ffices are inevitable.
• Mobile phone-based system

– Advantage

• solve the disadvantage of the OCR-based system
• improve the reliability of the 5W1H information.

– Disadvantage

• Need of practice of mobile phone operation ?

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• 3. Succession of farming skill by ICT
• Learning from skilled operator

– decrease of skilled operator – increase of new employee who doesn't experience labor for farming – personnel training become an important issue in farm management

• FVS: Farming visualization system enables

– Recording & visualization of detailed & comprehensive information of farming operation

• Purpose of FVS: Farming visualization system for

personnel training

– Visual textbook based on the image data of skilled operator – Scientific comparison of farming operation of skilled operator and non-skilled operator

• Better understanding of skilled operation
• Standardization of farming operation

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FVS: Farming visualization system

Visualization of integrated information of farming operation obtained from RFID, GPS, cameras and etc.

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Concept of the system

How should the system be developed to apply for practical use ? ・ Not interrupt farmer’s activities during farming operations ・ Be easy, quick and simple to use for non-experts (farmers) ・

Be available under many conditions without changing facilities

・ Monitor detailed farming operation and various conditions Wearable device equipped with RFID reader, GPS, camera and motion sensors ・ Make effective use of monitoring information in real-time

Previous system such as cell-phones and PDAs require farmers to input data Computers are troublesome task for farmers, especially the elderly Improvement of facilities requires considerable effort, space, and cost Understand operations’ effect and promote efficient management Provide support applications in response to recognized farming operations

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FVS: Hybrid type with D-GPS, Cameras, RFID readers, HMD and note-PC

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FVS: RFID readers type with PDA

RFID reader RFID reader

Wireless LAN AP

PDA PDA Tag Tag

Wireless Wireless LAN LAN

Data Reading Data sending RFID Reading record DB

Wear arab able le device device

Database server

Objects with RFID tags (Pesticide bottle, spray, etc.) PDA RFID reader

Tag Tag

Electroma Electromagnetic type netic type (13.56MHz) WIT-150-T, Welcat Inc. Microwave type (2.45 Microwave type (2.45GHz) Hz) µ-chip reader, Hitachi Ltd.

Patenting settlement Two types of RFID reader

Bluetooth

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Operation Recognition Method with RFID

• 1. RFID tags are attached to various objects
• 2. Perform farming operation with a wearable device
• 3. Analyze combination of detected RFID tags & sensor data

spray control for precision farming greenhouse access control

• peration

record of farm machinery material management

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Discussion and Future work

Does the wearable system works well ?

・ Automatic recognition of farming operation is almost achieved, but there were some failed detections of RFID tags and sensors

Adequate range design, tag allocation, other sensors

・ We chose the pattern matching method as an estimation algorithm. It is required to improve the algorithm on the basis of the situations

Bayesian estimation, support vector machine …(Poster 54)

・ Poor following capability of camera, awkward wearable display diminish adequate support application depending on circumstances

It is important to customize and modify these devices

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Discussion and Future work

Potential and future directions of this system ・ sharing databases regarding operation techniques Users’ feeling and notice as a autonomous sensor node ・ various navigation- and attention-system related to operations

・ user-viewed monitoring system selectively measures important objects

Important tool for understanding practicing farmers Enhance farmers’ sensitivity, judgment, and activity

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• 4. Concluding remarks
• Agriculture is facing big social and economic risks as well as natural

risks.

• Inappropriate or illegal agro-chemical use is a big risk for farm

management

– Our research shows that to manage this risk, ICT is useful and effective. – ICT will play an important role in managing other agricultural risk.

• Another critical issue in farm management is personnel training

– Fro better understanding of skilled operation the following are necessary

• Visual textbook based on the image data of skilled operator
• Scientific comparison of farming operation of skilled operator and non-skilled
• perator

– ICT will play an important role in these researches.

• To develop agriculture, an innovation is necessary in both cultivation

technology and farm management

• ICT has big potential of agricultural innovation.

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References

• FAO(2007)Guidelines "Good Agricultural Practice for Family Agriculture",

http://www.fao.org/docrep/010/a1193e/a1193e00.htm

• Fukatsu, F. and T. Nanseki(2009) Monitoring System for Farming Operations with

Wearable Devices Utilized Sensor Networks, Sensors 2009, 9, 6171-6184

• Fukatsu, F., K. Sugahara, T. Nanseki, S. Ninomiya(2009) Prototype System of

Monitoring Farm Operation with Wearable Device and Field Server, EFITA2009

• Nanseki, T. et.al.(2004) Development of a pesticide propriety use judgment server
• system. Agricultural Information Research, 13(4): 301-316 (In Japanese with English

abstract)

• Nanseki, T. et.al.(2005) A Navigation System for Appropriate Pesticide Use: Design and
• Implementation. Agricultural Information Research, 14(3): 207-226 (In Japanese with

English abstract)

• Nanseki, T (2005) A Navigation System for Appropriate Pesticide Use and Food Safety,

Proceedings of International Seminar on Technology Development for Good Agriculture Practice in Asia and Oceania 2005. 134-152

• Nanseki, T. et.al.(2006) Development of a Risk Management System for Agricultural

Chemical use, Agricultural Information Research, 15(4): 359-371 (In Japanese with English abstract)

• Nanseki, T. et.al.(2007) A Risk Management System for Agrochemical Use Design,

Development and Applications, Proceedings of the 6th Biennial Conference of the European Federation of IT in Agricultural, EFITA WCCA 2007 CD (ISBM10-1-905866-10- 0/13-978-905866-10-6).

• Nanseki, T. Yokoyama, K. (2008) Improve Food Safety amongst Food Operators, Ian

G.Smith and Anthony Furness Ed."Food Traceability around the World", 46-65.

• Sugahara, K. T. Nanseki and T. Fukatsu(2009)Prototype System to Recognize

Agricultural Operations Automatically Based on RFID, EFITA2009

THANKS FOR YOUR ATTENTION