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AgTech FoodTech

Attractive opportunities in Artificial Intelligence in Agriculture Market

Agriculture and farming is one of the oldest and most important professions in the world. Humanity has come a long way over the millennia in how we farm and grow crops with the introduction of various technologies. By 2050, the planet’s population is likely to rise to 9.7 billion, a rise of 2 billion from now. Along with increase in population, there is a substantial increase in the lifestyle. Those people will not only need to eat, they will want to eat better than people do now, because of higher incomes. However, only 4% additional land will come under cultivation by then.

In this context, use of latest technological solutions to make farming more efficient, remains one of the greatest imperatives. Farming is becoming a branch of matrix algebra. Farm operations involve a set of variables, such as the weather, soil’s moisture levels and nutrient content, competition to crops from weeds, threats to their health from pests and diseases, and the costs of taking action to deal with these things. If the algebra is done correctly, the yield gets optimised resulting in maximization of profit.

Agriculture is seeing rapid adoption of Artificial Intelligence (AI) and Machine Learning (ML) both in terms of agricultural products and in-field farming techniques. While Artificial Intelligence (AI) sees a lot of direct application across sectors, it can also bring a paradigm shift in how we see farming today. The industry is turning to AI technologies to help yield healthier crops, control pests, monitor soil and growing conditions, organize data for farmers, help with workload, and improve a wide range of agriculture-related tasks in the entire food supply chain.

The overall AI in agriculture market is projected to grow from an estimated USD 1.0 billion in 2020 to USD 4.0 billion by 2026, at a CAGR of 25.5% between 2020 and 2026. The market growth is propelled by the increasing implementation of data generation through sensors and aerial images for crops, increasing crop productivity through deep-learning technology, and government support for the adoption of modern agricultural techniques.

Markets and Markets

Recent Developments in AI in Agriculture include:

  1. South African agri-tech startup Aerobotics raised US$5.5 million in funding from Naspers Foundry. Cape Town-based Aerobotics, uses aerial imagery from drones and satellites, and blends them with machine learning algorithms. The startup’s cloud-based application Aeroview provides farmers with insights, scout mapping and other tools to mitigate damage to tree and vine crops from pest and disease.
  2. Insurance Australia Group has bought a multimillion-dollar stake in Digital Agriculture Services. Digital Agriculture Services is a rural technology company based in Melbourne. The company is applying machine learning and AI to develop rural data-powered solutions that transform the way rural assets are assessed, valued and monitored.
  3. Yanmar R&D Europe, with its European research facility based in Florence, Italy, focuses on a variety of field-based studies to bring added value to the agriculture industry. This include the two-year, four-million Euros ‘SMASH’ (Smart Machine for Agricultural Solutions Hightech) project being carried out in cooperation with 10 technology partners to develop a mobile agricultural ‘eco-system’ to monitor, analyse and manage agricultural crops.

Some of the companies active in AI in agriculture includes International Business Machines Corp., Deere & Company, Microsoft Corporation, Farmers Edge Inc., The Climate Corporation, Descartes Labs, Inc., AgEagle Aerial Systems, aWhere Inc., Gamaya Inc., Precision Hawk Inc., Granular, Inc., Prospera Technologies, Cainthus Corporation, Taranis, Resson Inc., FarmBot Inc., Connecterra B.V., Vision Robotics Corporation, Harvest Croo, LLC, Autonomous Tractor Corporation, Trace Genomics, Inc., VineView, CropX Inc., Tule Technologies Inc., Blue River technology, FarmBot and PEAT GmbH .

Categories
AgTech Blockchain

Blockchain for Food and Agriculture

Blockchain is an emerging technology allowing universal transactions among distributed parties, without the need of intermediaries. Blockchain is not a single technology but uses a combination of technologies that have a considerable history in computer science and in commercial applications like public/private key cryptography, cryptographic hash functions, database technologies especially distributed databases, consensus algorithms, and decentralised processing. Blockchain could pave way for a transparent supply chain of food, by facilitating the sharing of data between disparate actors in a food value chain.

Despite huge positives of the technology and the great interest it has received from public and private parties in general, some critical questions like accessibility, governance, technical aspects, policies, data ownership and regulatory frameworks need to be addressesed for its mass adoption.

Some common ways in which blockchain is applied in food and agriculture value chains are

Supply Chain Traceability: It enables companies to quickly track unsafe products back to their source and see where else they have been distributed. This can prevent illness and save lives, as well as reducing the cost of product recalls.

Example: Aglive – An Australian livestock tracking platform, has completed a pilot that monitored shipments of beef to China using blockchain. The pilot saw cattle tracked from Macka’s cattle farm in regional New South Wales to an abattoir located in the same state. From there, frozen beef products were tracked across the supply chain as the meat was transported by land freight interstate to Queensland, and then shipped to Shanghai — ensuring that the products were stored under safe conditions throughout transit. The products were then distributed to grocery stores in Shanghai.

Agricultural Commodities Trade: Commodities management involves deal documents, contracts, letters of credit, supply chain finance, traceability and government certifications. Blockchain is enabling these data management challenges and payment time lags.

Example: AgriDigital – A blockchain-based and integrated commodity management solution for the global grains industry.

Digital Marketplace: Digital marketplaces allow buyers and growers to connect directly, increasing the amount of profits that go to the farmers, and investors to invest directly into farms producing commodities and then trade on that investment.

Example: Twiga Foods Ltd – The company, buys fresh produce from 17,000 farmers and processed food from manufacturers and then delivers it to 8,000 vendors, most of whom are women.