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Role of Participatory Artificial Intelligence Strategies for Agricultural Systems

Agriculture domain is facing many challenges from planting to harvesting due to disease and pest infestation, inappropriate soil management, insufficient irrigation and drainage facilities, climatic irregularities and so on. Traditional methods used by farmers sufficient enough to serve the increasing demand and the soil by using harmful pesticides in an intensified manner. This affects the agricultural practice a lot and in the end the land remains barren with no fertility. These results in severe crop loss as well as environmental pollution due to excessive use of chemicals. Agriculture is a dynamic field where solution to a particular problem cannot be generalized. The different automation practices like IOT, Wireless Communications, Machine learning and Artificial Intelligence, Deep learning. There are some areas which are causing the problems to agriculture field like crop diseases, lack of storage management, pesticide control, weed management, lack of irrigation and water management and all this problems can be solved by above mentioned different techniques. Artificial intelligence (AI) techniques capture complex details of a situation to provide solution that best fits a particular issue. This applications of artificial intelligence techniques in agriculture multi-dimensional development of AI. This paper deals with Role of Participatory Artificial Intelligence Strategies for Agricultural Systems

Introduction

The AI is a advance technology in this digital world, we humans have pushed our limit  of the thinking process and are trying to coalesce normal brain with an artificial one. This continuing exploration gave birth to a whole new field Artificial intelligence. It is the process by which a human can make an intelligent machine. AI comes under the domain area of computer science which can be able to discern its milieu and should thrive to maximize the rate of success. AI should be able to do work based on past learning. Deep learning, CNN, ANN, Machine learning are certain domains which enhances the machine work and helps to develop a more advance technology. The term IOT is elucidated as “thing to thing” communication. The three main targets are communication, automation and cost saving in the system and also provides the in-depth application of IOT in the field of agriculture and how it can be helpful to the humans.

AI has penetrated in medical science, education, finance, agriculture, industry, security and many other sectors. Implementation of AI involves learning process of machines. There are many applications which exist today which includes analyzing of data from past data and experience, speech and face recognition, weather prediction, medical diagnostics. As AI stimulated, many new logics and method were invented and discovered which makes the process of problem- solving more simple.

Also Read: Organic Farming: A New Revolution In Agriculture

Artificial neural networks in agriculture

Artificial neural networks have been incorporated in the agriculture sector many times due to its advantages over the traditional systems. The main benefit of neural networks is they can predict and forecast on the base of parallel reasoning. Together expert systems and Artificial neural networks in predicting nutrition level in the crop. Instead of thoroughly programming, neural networks can be trained. Use of ANN system is built on a single chip computer. Neural networks always prove to be the best when it comes to predicting methods. The model is provide raw data like humidity, temperature, precipitation, cloud cover and wind direction.

Artificial neural networks in agriculture

Automation and wireless system networks in agriculture

 The agriculture sector had to adapt the inventions which came along in automation field. Embedded intelligence in agriculture sector includes smart farming, smart crop management, smart irrigation and smart greenhouses. It is necessary for a nation to include these growing technologies in agriculture sector for growth of a nation as many sectors are inter-dependent on agriculture. The system employed various sensors such as temperature sensor, leaf wetness sensors and humidity sensors in the vineyard. The additional advantage of this system is it also suggests the farmer pesticides and pacifies manual effort in the detection of disease. While a similar method of machine learning was employed in monitoring the growth of Paddy crops. This system was increasing the yield and productivity of paddy crops. It also proved to be cost effective and durable. In the fully-automated Farm of the Future, dedicated robots will take on farming jobs that once could be done only by people.

Automation and wireless system networks in agriculture

Automation and wireless system Future farming

Innovation

Manohar Sambandam, is the founding partner and CEO of Green Robot Machinery Private Limited. Manohar is a practicing farmer with agriculture land in Tamilnadu. He is engaged in farming paddy, cotton and legumes. He took to entrepreneurship in 2014 to solve his own pain point of labour availability in cotton farming.

GRoboMac (Green Robot Machinery Private Limited) is working to mechanize farm tasks by building smart machinery using 3D vision technology and robotics. The company’s first product is a cotton-picking machine that aims at reducing drudgery and dependency on farm labour while maintaining the quality and speed of human picking.

Automation Cotton-Picking Robot

Automation Cotton-Picking Robot

 Precision farming

Precision farming is a more accurate and controlled technique of farming which substitutes the repetitive and labour intensive part of farming, besides providing guidance regarding crop rotation. This  distinguished  key  technologies  that enable  precision  farming  are high precision positioning system, geological mapping, remote sensing,  integrated  electronic communication, variable rate technology, optimum planting and harvesting time estimator, water resource management, plant and soil nutrient management, attacks by pest and rodents.

Challenges in AI adoption in agriculture

Although AI presents immense opportunities in agriculture application, there still prevails a deficiency in familiarity with advanced high tech machine learning solutions in farms around the world. Exposing farming to external factors like weather conditions, soil conditions and vulnerability to the attack of pests is high. AI systems too require  a  lot  of  data  for  training machines, to take precise forecasting or predictions. Just in case of a very large area of agricultural land, spatial data could be collected easily while getting temporal data is a  challenge.

Challenges in AI adoption in agriculture

Organic Farming PDF Book

Future scope for AI

 The farmers are young will make more investments in automation with much interest than the elder farmers. The technology which is new has to be introduced slowly with time. Slowly the agriculture sector is moving towards precision farming in which management will we done on the basis of individual plant. Deep learning and other extend methods are used to detect the plant or flower type, this will help farmers to provide favourable environment to the plant for sustainable growth. Artificial intelligence techniques are growing at a rapid scale and it can be used to detect disease of plants or any unwanted weed in the farm. Here, wireless technology and IOT comes in the run and using the latest communication protocols and sensors we can implement weather monitoring and control without human presence in the farm. Harvesting of fruits and crops can also be incorporated by robots which are specialized in working round the clock for quick harvesting. Application of robotics are vast in farming such as the robots can be used in seeding and planting, fertilizing and irrigation, crop weeding and spraying, harvesting and shepherding.

Future scope for AIConclusion

Need of automation in the agriculture sector is must and there are many ways it can be implemented in practice. Irrigation is the foremost thing where automation for optimal water usage. Soil moisture sensor helps to monitor the moisture level of the soil and starts watering the farm as the value get below the threshold level set by the farmer. The embedded system and Internet of Things help to develop a compact system which monitors the water level of the farm without human interaction. There are many different techniques that we can implement as automation through different forms like using Machine learning, Artificial Intelligence, Deep learning, Neural network, Fuzzy logic. The idea is to use any of these extended methods to reduce human intervention and human efforts.

References

  1. Al-Ghobari and Mohammad, 2011 Intelligent irrigation performance: evaluation and quantifying its ability for conserving water in arid region Appl Water Sci, 1 (2011), pp. 73-83.
  2. Dursun, S. Ozden, 2011 A wireless application of drip irrigation automation supported by soil moisture sensors Sci. Res. Essays, 6 (7), pp. 1573-1582
  3. Arif, M. Mizoguchi, B.I. Setiawan, R. Doi, 2012 Estimation of soil moisture in paddy field using Artificial Neural Networks International Journal of Advanced Research in Artificial Intelligence., 1 (1) (2012), pp. 17-21.
  4. Bargoti, J. Underwood, 2017 Deep Fruit Detection in Orchards, IEEE International Conference on Robotics and Automation (ICRA), pp. 3626-3633
  5. Bannerjee, U. Sarkar, S. Das, I. Ghosh, 2018 Artificial Intelligence in Agriculture: A Literature Survey International Journal of Scientific Research in Computer Science Applications and Management Studies., 7 (3) (2018), pp. 1-6.

 

Article Written By

Sathishwaran.R

PG. Scholar,

Department of Agricultural Extension, Annamalai University.

Email-sathishwaran.r2505@gmail.com

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