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SMART AGRICULTURE -FOR RESOURCE USE EFFICIENCY

III SEMINAR on

Presented by:

MUTTEPPA CHIGADOLLI

PALB 8031

III Ph.D. Scholar

“It is not the quantity of water applied to a crop, it is the quantityof intelligence applied which determines the result -there is moredue to intelligence than water in every case.”

-Alfred Deakin (1890)

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OUTLINEIntroductionObjectivesConcept of smart agriculturePredictions for global smart agricultureTransformative discoveries for smart agricultureSmart agricultural practices for higher resource use efficiency in farmingSmart agricultural practices for higher productivity and profitability in farming List of smart agricultural practicesPilot projects/ Government initiatives towards smart agricultureBenefits and challenges of smart agricultureTo review the related studies Role of extension in smart agricultureConclusionMutteppa Chigadolli

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INTRODUCTION:

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Land Degradation Depletion of water resources Water pollution

Labour cost Climate change Decline Productivity

INTRODUCTION:

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INTRODUCTION:

Green Revolution Technology Revolution

Double the Agriculture

Production by 2050.

Reduction in Wastage and losses

by 50 % (FAO)Lesser impact on

environment

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OBJECTIVES:

1. To understand the concept of Smart Agriculture

2. To deliberate smart agricultural practices for higher farm resource use efficiency

3. To review the case studies / research studies related to smart agriculture and their implications

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1. CONCEPT OF SMART AGRICULTURE

Smart Agriculture involve theintegration of advancedtechnologies into existing farmingpractices in order to increaseproduction efficiency and thequality of agricultural products.

Smart agriculture deals withapplying inputs (what is needed)when and where it is needed, hasbecome the third wave of themodern agriculture revolution.

SMART AGRICULTURE

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1. CONCEPT OF SMART AGRICULTURE

SMART AGRICULTURE

= Precision Agriculture +

Smart Farming + Digital Farming

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SMART AGRICULTURE PRACTICES

• SMART AGRICULTURE PRACTICES: refers to the all the practices used in smart

agriculture which may be tools, techniques, web applications, mobile apps, GIS,

GPS, Decision Support System, Expert System, Implements Handling techniques etc

which helps in increasing agricultural production, productivity with optimimum

resource utilization and lesser impact on environment.

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TOP 10 TECHNOLOGIES IN SMART AGRICULTURE

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TRANSFORMATIVE DISCOVERIES FOR SMART AGRICULTURE

1. Internet of Things (IoT):IoT is described as a networkof physical objects. Thesecan be “things” that can beembedded with technologies,software or sensors whichfurther helps in connecting orthe exchange of data withother devices or systems viathe internet or vice versa.More than 8.3 million“things” got connectedevery day,

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2. ARTIFICIAL INTELLIGENCE (AI):

• It is the science of instilling intelligencein machines so that they are capable ofdoing tasks that traditionally requiredthe human mind.

• The term AI is commonly used when amachine mimics cognitive functions suchas planning, learning, reasoning, problemsolving, knowledge representation,perception, motion, manipulation, socialintelligence, and creativity.

• AI combines automation, robotics, andcomputer vision.

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3. BLOCKCHAIN:• Blockchain: It is a recent

technological advancement withpotential for addressing thechallenge of creating a moretransparent, authentic, andtrustworthy digital record of thejourney that food and otherphysical products take across thesupply chain.

• Blockchain works by mapping dataand providing it to users along thevalue chain simply by scanning abarcode. These barcodes areapplied and linked throughout thevalue chain automatically bygrading and sorting robotics.Mutteppa Chigadolli

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• A machine resembling a human being andable to replicate certain human movementsand functions automatically.

• Drones with AI-enabled vision processingcapabilities are being used to assess the realsituation on the condition of crops onground.

• Autonomous drones and the data theyprovide can help in crop monitoring, soilassessment, plant emergence and population,fertility, crop protection, crop insurancereporting in real time, irrigation and drainageplanning and harvest planning.

4. ROBOTICS:

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5. AUTONOMOUS SWARMS:

• Autonomous swarms = Swarmrobotics+ Blockchain.

• Swarm robotics involves multiplecopies of the same robot, workingindependently in parallel to achieve agoal too large for any one robot toaccomplish.

• With autonomous swarms, pesticideand fertilizer can be applied moresparingly and planting and harvestingcan be done with individual attentionto each plant.

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6. Artificial Intelligence of Things (AIoT):• AIoT is a combination of AI

and IoT.

• AI can complete a set oftasks or learn from data in away that seems intelligent.

• Devices empowered with thecombination of AI and IoTcan analyze data and makedecisions and act on thatdata without involvementby humans

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• Big Data: It is a combination oftechnology and analytics that cancollect and compile novel data andprocess it in a more useful and timelyway to assist decision making.

• Big data provides farmers granulardata on rainfall patterns, water cycles,fertilizer requirements, and more.This enables them to make smartdecisions, such as what crops to plantfor better profitability and when toharvest. The right decisions ultimatelyimprove farm yields.

7. BIG DATA:

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2. To deliberate smart agricultural

practices for higher farm resource use

efficiency

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• Seed sowing at right place andright amount is very tedious infields.

• Effective seeding requires controlover two variables: planting seedsat the correct depth, and spacingplant at the appropriate distanceapart to allow for optimal growth.

• Precision seeding equipments aredesigned to maximise thesevariables every time.

Pneumatic precise rice seeder and fertilizer applicator

1. Precision in Seed Sowing and Planting

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A. Smart Fertilizers: Smart fertilizersare new type of fertilizers which areformulated based on micro-organismsand nano-materials.

• Nanotechnology based smart fertilizersdevelopment with an emphasis oncontrolled- release will synchronizenutrient availability with the plantdemands thereby reducing nutrientlosses.

• Reduced - phosphate by 50 to 25 % andincreased yields by 10 percent.

• Smart micronutrients the reduction indose was up to 90 percent.

• Farmers’ income can be raised by 15-20percent.

2. PRECISION IN NUTRIENT MANAGEMENT

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Leaf color is a fairly good indicator of the

nitrogen status of plant.

The leaf colour chart developed by

International Rice Research Institute,

Phillipines.

The monitoring helps in the determination

of right time of nitrogen application.

The studies indicate that nitrogen can be

saved from 10-15 percent using the leaf

colour chart. ( Singh et al., 2015)

B. Leaf Color chart:

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C. NDVI SENSORS:

• Studies in wheat as well as in

rice crops have shown that need

based nitrogen application

using remote sensing based

Normalised Difference

Vegetation Index (NDVI)

sensors can save 15-20 percent

nitrogen without any yield

penalty (Bijay et al., 2015)

leading to improved profit

margins to the farmers.Mutteppa Chigadolli

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SPAD (Soil-Plant Analysis Development) is a

simple, quick and portable diagnostic tool for

monitoring leaf nitrogen (N) status and

improving the time of N topdressing in rice.

SPAD is low cost chlorophyll meter and

affordable by farmers.

It is possible to monitor leaf N status using the

SPAD thresolds and guide fertilizer-N timing on

irrigated rice.

D. SPAD Value:

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D. NUTRIENT EXPERT (NE):

• NE is the recently developed precisionnutrient management technology guided bydecision-support system software forimproving crop yields.

• International Plant Nutrition Institute (IPNI)in collaboration with CIMMYT hasdeveloped a Nutrient Expert (NE),

• It is a nutrient decision support system,based on site-specific nutrient management(SSNM) principles. NE provides fertiliserrecommendations by considering yieldresponses and targeted agronomic efficienciesalong with contribution of nutrients fromindigenous sources.

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Web/ Mobile based services

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E. UREA DEEP PLACEMENT (UDP):

• UDP technique, developed by the IRRI &IFDC.

• In the UDP technique, urea is made into“briquettes” of 1 to 3 grams that are placedat 7 to 10 cm soil depth after the paddy istransplanted.

• This technique decreases nitrogen losses by40 percent and increases urea efficiency by50 per cent.

• It increases yields by 25 percent with anaverage 25 percent decrease in urea use(Singh et al 2010).

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• Pressurizedirrigation systemslike sprinkler, dripand subsurfacedrip irrigation arealready prevalentirrigation methodsthat allow farmersto control whenand how muchwater their cropsreceive.

3. EFFICIENT WATER MANAGEMENT PRACTICES

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1. Sensor-based Control: This method leveragesreal-time measurements from locally installedsensors to automatically adjust irrigationtiming to the exact temperature, rainfall,humidity and soil moisture present in a givenenvironment. This data is also supplementedwith historic weather information to ensurefarmers are able to anticipate unfavorableconditions.

2. Signal-based Control: Unlike sensor-basedcontrols, these smart irrigation systems rely onweather updates transmitted by radio,telephone or web-based applications. Thesesignals are typically sent from local weatherstations to update the “evapotranspiration rate”of the irrigation controller.

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1. New Generation Herbicides: Recently some postemergence new generafion herbicides areavailable in the market with the assurance ofselective effective control of weeds in field crops.These herbicides are required in very low doses.Ex: Tembotrione in maize, Pyrazosulfuron ethylin rice; Clodinafop + Metsulfuron methyl inwheat are found very effective to control bothbroad leaved and grassy weeds.

2. Herbicide Resistant crops (HRCs): Herbicideresistant crops are genetically modified (GM)crops engineered to resist specific broad –spectrum herbicides, which kill the surroundingweeds, but leave the cultivated crop intact. Ex:Maize, Soyabean and Cotton have beenengineered for glyphosate.

4. WEED AND PEST MANAGEMENT PRACTICES

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C. ARTIFICIAL INTELLIGENCE AND AUTOMATION IN WEED MANAGEMENT:

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5. RESOURCE CONSERVING PRACTICESi) Laser land levelling: Saving of 20-25 percent of irrigation water apart from several otherbenefits like better crop establishment, nutrient use efficiency, uniform irrigation etc. have beenreported with laser land levelling.

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ii) Raised-bed Planting: Raised-bed plantingrefers to growing of crops in row geometryand on raised beds with furrow irrigationarrangements using a multi-crop raised bedplanter. Helps in saving irrigation water by 30-40 percent.

iii) Conservation Tillage: Conservation tillagepractices range from zero tillage (No-till),reduced (minimum) tillage, mulch tillage, ridgetillage to contour tillage.Conservation tillage farming is a way ofgrowing crops without disturbing the soilthrough tillage using zero-till planter/drill.

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6. PRACTICES FOR HIGHER PRODUCTIVITY AND PROFITABILITY

Crop diversificationConservation Agriculture

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MULTILAYER FARMING• Multilayer farming can be scientifically defined as an integrated agricultural system

in which we plant (4-5) different types of crops on same land and at same time whichmatures at different height and in different time.

• Example: Colocasia, Potato And Leafy Vegetable like coriander

Crops Planting time Germination time

Depth of sowing

Maturity time

Colocasia January 2-3 months 25-30 cm 7-8 monthsPotato 20-30 days 10-15 cm 2-3 monthsLeafy vegetables

5-8 days 3-6 cm 15-25 days

Papaya 7-8 months for fruit setting

15-20cm 9-11 months

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AKASH CHAURASIA

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HYDROPONICS• Vertical farming is the practice of

growing crops in vertically stackedlayers. It often incorporatescontrolled-environment agriculture,which aims to optimize plant growth,and soilless farming techniques suchas hydroponics, aquaponics, andaeroponics.

• Hydrophonics: Soil is replaced by awater solution that is rich inmacronutrients like nitrogen,potassium, phosphorous, calciumnitrate and micronutrients likemanganese, zinc etc. A ‘grow system’controls the balance of nutrition,humidity and temperature, uses lesswater than soil-based farming andincreases yield without chemicals orpesticides.

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SOIL MOISTURE & pH METER

•3 in 1 Soil Tester: Measure soil'smoisture, pH and light by just pluggingin the probe based on reading you candecide when to water, control pH level,determine if plant getting adequate light.

•Simply insert probe of the meter into thesoil to remaining about 10mm outsider,switch to the setting you want to measureand read the scale.

• For example: Choosing the MOIST,scale of 1-3 (red parts)means needingwatering, 4-7(green parts) meanssuitable, 8-10 (blue parts) means that toowet.Mutteppa Chigadolli

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INFOSYS MODEL FOR SMART AGRICULTURE

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LIST OF DIFFERENT SMART AGRICULTURE PRACTICESSI. No

Technologies Mobile/Web Applications

Devices Methods

1. Artificial intelligence Plantix Urea Deep placement Conservation agriculture2. Blockchain Kisan Suvidha SPAD Meter Hydroponics3. Internet of Things Pusa krishi Soil moisture & pH meter Multilayer farming

4. GIS Expert Systems Leaf color chart Organic farming5 GPS DSS Pneumatic planter Mulching6. Smart fertilzers M-kisaan portal Kisan raja-Motor controller Vertical farming7. Sensors Websites Deep thunder IFS8. AIOT, Big data Plant doctor Raised bed planting9. Robots, Drones,

Swarms

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Smart Agriculture Practices for India

Soil moisture & pH

meter

Kisan Raja-motor controller Grid Sampling

Pressurized Irrigation

(Smart Irrigation)

Mulching Smart fertilizers

Leaf color chart Multilayer farming Mobile & Web based

applications

Pneumatic planter Smart agronomic practicesMutteppa Chigadolli

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DATA DRIVEN TOOLS FOR SMART AGRICULTURE TOOLS FEATURES Global positioning system • Location of soil samples and the laboratory results can be

compared to a soil map.• Fertilizer and pesticides can be prescribed to fit soil properties

(clay and organic matter content) and soil conditions (relief anddrainage)

• One can monitor and record yield data as one goes across the field.

Global information system • Spatially Referenced Geographical Information

Grid sampling • Determination of precise nutrient doses

Variable rate Technology • precisely control the rate of application of crop inputs that can be varied in their application commonly include tillage, fertilizer, weed control, insect control, plant population and irrigation.

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TOOLS FEATURESYield Monitoring • yield data from the monitor is recorded and stored at

regular intervals along with positional data received from GPS unit.

Remote sensing • Crop Production Forecasting• Soil Mapping• Wasteland Mapping• Water Stress • Insect Detection• Nutrient Stress

Auto-Guidance Systems • Allows more precise automated application of inputs

Proximate guidance systems • Proximate sensors can be used to measure soil (N and pH) and crop properties as the tractor passes over the field.

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INITIATIVES OF SMART AGRICULTURE IN INDIA• NITI Aayog --National Strategy for Artificial

Intelligence in India, -economic growth and socialinclusion.

• MOU with IBM to use AI to secure the farmingcapabilities of Indian farmers- To provide weatherforecast and soil moisture information to farmers totake pre-informed decisions regarding better managementof water, soil and crop.

• To promote innovative technologies in agriculture sector,the AGRI-UDAAN is launched to mentor 40 agriculturalstart-ups and enable them to connect with potentialinvestors.

• Maha Agri Tech Project in Maharashtra- to addressvarious risks related to cultivation such as poor rains, pestattacks, etc., and to accurately predict crop yielding.

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• MOU between GoK and Microsoft to empower smallholder farmers with AI-based solutions to helpthem increase income and price forecasting practices.

• Kerala with Cisco to develop the Agri-Digital Infrastructure Platform and provide access to e-learning and advisory services to farming and fishing communities in Kannur district.

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PREDICTIVE ANALYTICS APP

• In India, Microsoft collaborated with ICRISAT (International Crops Research

Institute for Semi Arid Tropics) developing a predictive analytics app that calculated

the best crop sowing date for maximizing the yield.

• As a test case, farmers across seven villages were sent text messages with dates for

sowing and other advice.

• Despite meagre rainfall, farmers that used the app boosted their yields by 30

percent.

• When other farmers witnessed the results, they were also more likely to use the app

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Benefits of smart Agriculture

Improved monitoring,

scouting and controlAccurate data

collectionPrecise analytics for

prompt actionsImproved return on

investmentIncrease in yield

quality and quantity

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CHALLENGES OF SMART AGRICULTUREHigh setup and

maintenance cost

Flight time limitations and

coverage

Weather dependency/climate

dependency

Laws and regulationsFragmented lands

limits the smart agriculture

Needs expertise in handling devices

Complete dependency on data/electricity

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ROLE OF EXTENSION IN SMART AGRICULTURE

Conducting field days

Conducting Demonstrations

Field Exposure visits

Trainings to farmers

Organizing farmer field schools

Collective/ Cooperative

farming

Facilitating collaboration of technological institutes with

agricultural institutes

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3. TO REVIEW THE STUDIES RELATED TO SMART AGRICULTURE

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1. IMPACT EVALUATION STUDY OF NATIONAL MISSION ON MICRO IRRIGATION (NMMI)

2014

Govt. of India, (Global Agri System)

13 States and 64 districts (MH, GUJ, KTK, TN, AP, UP, BIHAR, RAJ, ODISHA, CHATTISGARH, SIKKIM, UTTARAKHAND AND HARYANA)

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Irrigation cost reduced by 20% to 50% with average of 32.3%.

Electricity consumption reduced by about 31%.

28 percent reduction in total fertiliser consumption in the surveyed states.

Average productivity of fruits and vegetables increased by about 42.3 % and 52.8%.

Overall income enhancement of farmers was in the range of 20% to 68% with an average of 48.5%.

Impacts of

NMMI

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CASE STUDY 1. AGRIBOT: SAVING WATER AND SPRAYING PESTICIDES

2020

Nimish Kapoor

In locust outbreak areas India

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• In conventional method up to 400 litres ofwater is used for spraying pesticides in one acrefield, the Agribot can spray it in 8 litres ofwater.

• If pesticide spraying is made mandatory bydrone, about 1.5 lakh crore litres of water canbe saved.

• Amidst the terror of the locust attack, inJanuary 2020, the drone sprayed over 500hectares of land in 16 days and freed the areafrom locusts.

• It takes about 3-5 minutes for a drone to sprayon one acre of land.

• The Agribot drone can cover 50 acres a daywith additional batteries.

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• The use of pesticides is 15 to 35percent higher efficient with dronesthan the conventional methods as theamount of chemical is scientificallydetermined.

• By spraying pesticides with drones,farmers stay away from chemicalsand they do not have any side effectson their health.

• They are also able to operate ininaccessible areas and mountains. Inthe middle and later stages of the cropthe drone can enter the fields forspraying pesticides, whereas this is notpossible with heavy equipment.

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CASE STUDY 3: ARTIFICIAL INTELLIGENCE BASED PILOT PROJECT IN KURNOOL

• In 2016, Microsoft, in partnership with ICRISAT initiated a pilot project in DevanakondaMandal in the Kurnool district of AP.

• The pilot had a sample base of 175 farmers who were alerted on their mobile phones aboutsuitable cropping dates, land preparation, and soil test-based fertilizer utilization.

• This helped increase crop output by around 30%.

• In 2017, this project was expanded to cater to approximately 3,000 farmers in Karnatakaand Andhra Pradesh during the Kharif cycle for a host of crops like groundnut, ragi,maize, rice, and cotton, among others.

• The increase in crop yield following the AI intervention ranged from 10-30% across allcrops (Nagpal, 2017).

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CASE STUDY 3: MOBILE MOTOR CONTROLLER DEVICE- KISAN RAJA

• Vijay Bhaskar Reddy

• Developed an IoT based autonomousirrigation solution, Mobile Motor ControllerDevice- Kisan Raja

• Kisan Raja which helps farmers monitor,control and utilise water judiciously.

• This device has helped more than 34,200farmers across ten states namely Telangana,Andhra Pradesh, Karnataka, Maharashtra,Tamil Nadu, Haryana, Punjab, Rajasthan,Madhya Pradesh and West Bengal.

• Kisan Raja reduced water consumption by30 percent while improving land managementdecisions.Mutteppa Chigadolli

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MOBILE MOTOR CONTROLLER

DEVICE

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CONCLUSION

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