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AI and Machine Learning are Transforming Indian Agriculture

Agriculture plays a vital role in India’s market. Over 58 per cent of those rural households depend on agriculture as their primary way of livelihood, according to an IBEF report. Agricultural exports constitute 10 per cent of the country’s exports and are the fourth-largest exported first commodity class in India.

 

As stated by the Department of Industrial Policy and Promotion (DIPP), the Indian agricultural services and agricultural machinery industries have cumulatively attracted Foreign Direct Investment (FDI) equity inflow of about $2.45 billion and the food processing sector has attracted approximately $7.81 billion during April 2000 to June 2017.

 

On the back of increased FDI and conducive government initiatives, the agriculture industry is increasingly looking at ways to leverage technology to get better crop yield. Many tech companies and startups have emerged in the last couple of years with concentrated agri-based solutions which benefit from farmers.

Advantage of implementing AI in Agriculture

Using Artificial intellect in agriculture helps the farmers to comprehend the data insights such as temperature, precipitation, wind speed, and solar power. The data analysis of historical values offers a better contrast of the desirable outcomes. The best aspect of implementing AI in agriculture which it won’t eliminate the jobs of human farmers; instead, it will boost their procedures.

 

 

  • AI provides more efficient methods to produce, harvest and market essential plants.
  • AI implementation emphasis on checking defective crops and enhancing the prospect of healthy crop production.
  • The development of Artificial Intelligence technology has bolstered agro-based companies to operate better.
  • AI is being used in applications such as automated machine adjustments for weather forecasting and pest or disease identification.
  • Artificial intelligence can enhance crop management practices, consequently helping many tech companies invest in algorithms which are becoming useful in agriculture.
  • AI solutions can address the challenges farmers face, such as climate variation, an infestation of pests and weeds that reduces yields.

Effect of Artificial Intelligence in Agriculture

AI technology is rapidly rectifying the problems while recommending specific action that’s required to overcome the issue. AI is useful in tracking the information to find answers quickly. Let us see how AI is used in agriculture to enhance outcomes with a minimal environmental price. By executing AI can identify a disease with 98 per cent accuracy. Therefore, AI helps farmers track the vegetable and fruit by correcting the light to hasten generation.

 
pianalytix machine learning in agriculture

Lets explore applications of artificial intelligence (AI) to give business leaders with the understanding of present and emerging trends, and present representative examples of popular apps.

 

Based on our research, the most Well-known applications of AI in Indian agriculture appear to fall into three Big classes:

 

 

  1. Crop and Soil Monitoring — Companies are leveraging sensors and various IoT-based technology to monitor plant and soil health.
  2. Predictive Agricultural Analytics — Various AI and machine learning tools are being used to predict the optimal time to sow seeds, get alerts on risks from pest attacks, and more.
  3. Supply Chain Efficiencies– Businesses are using real-time information analytics on data-streams coming from several sources to build an efficient and smart supply chain.

In the entire article below, we will explore each category of AI software in the agricultural industry, along with representative companies and use cases.

 

1) Crop and Soil Monitoring

 

CropIn — Applying AI to Boost per-Acre Value

 

CropIn is a Bengaluru-based startup which claims to be an intuitive, intelligent, and self-evolving system which provides future-ready farming alternatives to the agricultural industry.

 

To explain the benefits of Cropin’s technologies, the organization cites a use case with one of the world’s largest producers of potato specialities company based in India which leases plots for farming and has 2500+ parcels spread across a place of 5200+ acres. Earlier, they used to record farm data manually, thus generating multiple inconsistent entries.

 

Together with CropIn’s ‘smart farm’ solution, all the plots had been geo-tagged to discover the actual plot area. The answer helped in remote sensing and weather advisory, monitoring and tracking farm actions for whole traceability, educating farmers on the adoption of a right package of inputs and practices, monitoring crop health and crop estimation, and alarms on the pest, diseases etc..

 

Primarily, CropIn uses technologies like AI to help customers analyze and interpret data to derive real-time technical insights about standing crop and projects spanning geographies. Its agri-business intelligence alternative named SmartRisk” frees agri-alternate information and offers risk mitigation and forecasting for practical credit risk assessment and loan recovery help.

 

Proprietary machine learning algorithm constructed on satellite and weather information is used to provide insights at storyline and area degree,” Krishna Kumar, Founder & CEO, Cropin stated.

 

Intello Labs — Using Deep Learning for Picture Analysis

The company claims to provides innovative image recognition technology which can recognize objects, faces, flora fauna and tag them in any picture.

 

The business claim to use deep learning algorithms on which a new generation of intelligent applications are being assembled for applications including agriculture, eCommerce, advertising, manufacturing, and curation.

 

Small farmers around the world follow traditional farming practices due to lack of access to scientific comprehension of harvest lifecycle, pests, quality metrics and the latest micro-fertilizers. “Our Picture based alternatives provide insights on the crops’ health during the growing season and its final harvested quality by photograph,” the company claims on its website.

Intello Labs claims to supply:

Agricultural Product Grading: Automated quality analysis of pictures of food products is an accurate and reliable way of grading new products (fruits, grains, vegetables, cotton etc.) characterized by colour, size and shape. Their solution reads the picture a farmer has obtained on his telephone and determines the product quality in real-time, without any manual intervention. The solution uses deep learning and image processing units to recognize any crop diseases or insect infestation from the crops. Together with the parameters, it gives recommendations on how that disease could be treated and prevented by increasing further.

The company has no case studies or visual demos of the technology at this moment.

 

2) Predictive Agricultural Analytics

 

Microsoft India — AI-based Sowing Program

Determining the proper time to sow plants is often one of the biggest challenges for Indian farmers in which drought and excess rainfall can be both severe challenges.

 

The app sends sowing advisories to engaging farmers on the optimal date to sow. “The very best part — the farmers do not need to install any detectors in their fields or incur any capital expenditure.

To figure out the crop-sowing period, historical climate data (crossing over 30 years from 1986 to 2015) for the specific area in Andhra Pradesh was analyzed using AI. MAI is the standardized measure used for assessing the level of adequacy of rain and soil moisture to fulfil the prospective water requirement of crops.

 

As per the report cited previously, in a few dozen villages in Telangana, Maharashtra, and Madhya Pradesh, farmers receive automated voice calls alerting them if their crops are at risk of a pest attack according to weather conditions and stage of the harvest. No specific numbers on the outcomes were reported.

 

3) Agri Supply Chain

 

Gobasco — The Intelligent Agri Supply Chain

Vedant Katyar, co-founder & CEO of the Business, is an engineering graduate from premier Indian tech institute BITS Pilani. At the same time, CTO Abhishek Sharma is a PhD in Artificial Intelligence from the University of Maryland in the United States.

 

Gobasco claims to use real-time data analytics on data-streams coming from several sources across the nation aided with AI-optimized automatic pipelines to boost the efficacy of their present agri distribution chain radically. “Our data-driven online agri-marketplace gives the best prices for both the manufacturers and buyers at their fingertips.

 

Through our carefully engineered tech-driven pipeline, made for the Indian Agri supply-chain, we run at a more significant profit margin than the traditional companies,” the firm stated in its site.

 

Gobasco uses AI and related technologies in the respective stages of the Agri supply chain to make sure it’s efficient and quick. Some of them are listed below:

 

  • Transition Discovery: Real-time data analysis on multiple data-streams along with crowd-sourced data from producer/buyer marketplaces and transporters feeds their automatic trade discovery algorithm to acquire high-margin transactions.
  • Quality Maintenance: Computer vision and AI-based automated grading and sorting are done for veggies and fruits for creating an international agri-commodity standard for dependable trading across state boundaries.
  • Credit Risk Management: Crowd-sourced data, algorithms and analytics overcome the charge default problem, the hardest challenge of current supply-chain, to ensure a shallow risk operation.
  • Agri-Mapping: Deep-learning based satellite image analysis and crowd-sourced information fusion obtains a real-time agri map of commodities at a resolution of 1 sq-km.

 

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