According to the UN Food and Agriculture Organization, the population will increase by 2 billion by 2050. However, only 4% of additional land is likely to come under cultivation.
Here we discuss the usage of artificial intelligence in agriculture sector to make farming more structured and efficient. While the role of Artificial Intelligence in agriculture sees a lot of direct application across sectors, it can also bring a transformation in how we see farming today.
Prospera, Blue River Technology, FarmBot, and many other startup companies help farmers to know how AI can be used in agriculture and to do end-to-end farming all by themselves
According to BI Intelligence research, global spending on smart, connected agricultural technologies and systems, including AI and machine learning, is projected to triple in revenue by 2025, reaching $15.3 billion.
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What is artificial intelligence in agriculture
Artificial Intelligence in agriculture plays a prominent role in improving cultivation and farming thus helping agriculture-based economies to grow.
Crop and field cultivation can take benefit from the latest AI technologies like Automated Robotic Systems to optimize irrigation, crop monitoring, farming, automate spraying and exercise of pesticides and herbicides.
Artificial intelligence in Indian agriculture
Agriculture is the backbone and one of the oldest sectors in India. As India faces a lot of problems with climate change, population growth, and food security concerns every year, developing the Agriculture industry has become a huge concern for the Indian government.
Role of artificial intelligence in Indian agriculture has become a game changer and beneficial to the Indian economy.
Benefits of AI in agriculture
- AI powered devices analyze data from the farm through sensors and cameras and can advise farmers on where and when to plant crops.
- AI-powered cameras mounted on a tractor can automatically detect pests and spray pesticides in a targeted area, reducing the use of pesticides.
- AI powered machines can detect the pest in specific areas over time with implications for farm management.
Examples of Artificial intelligence in agriculture sector
Artificial intelligence in agriculture projects in India denotes the willingness of the Government of India to ease social prosperity through AI-powered farming in India.
- Government of India with IBM: Government of India has signed an MOU with IBM to use artificial intelligence in the Indian agriculture sector to make farming stable for Indian farmers. IBM’s Watson decision platform gives a farm-level solution for improving the agriculture sector. It provides weather forecasts and soil moisture information to farmers to make pre-informed decisions regarding better management of water, soil, and crop.
- Pradhan Mantri Fasal Bima Yojana (PMFBY): To improve the crop sector, the govt introduced a program called PFMBY which can currently conceive of the employment of innovative technologies like AI, remote sensing imageries, and modeling tools to reduce the delay for settling of claims of the farmers. By analyzing the data collected, the scheme aims at increasing crop insurance penetration in India by increasing farmer awareness and reducing farmer premium rates.
- PM-KISAN: The government of India has come up with a scheme — PM-KISAN, where farmers receive Rs. 6000 annually to support their farming skills. The government is aimed to invest the huge amount of data collected by several agri-schemes and use the same for betterment of farmers who need the benefit of PM-KISAN.
- Government of Karnataka with Microsoft: The Karnataka government has signed an MOU with a tech giant — Microsoft to empower smallholder farmers with AI-powered solutions to help them increase annual income and price forecasting practices. Microsoft with the right direction from Karnataka Agricultural Price Commission (KAPC) is aiming to use digital tools to develop a multilevel agricultural product price forecasting model considering the following parameters — sowing area, production and cultivation time, weather datasets, and other applicable data.
- AGRI-UDAAN: The government of India has also launched AGRI-UDAAN – Food & Agribusiness Accelerator 2.0 n a bid to push innovative technologies in agriculture secure to mentor 40 agricultural startups from cities like Chandigarh, Ahmedabad, Pune, Bengaluru, Kolkata and Hyderabad, and enable them to get connected with potential investors.
- Maha Agri Tech Project: This project is based out of Maharashtra — seeks to use innovative technologies to address various risks related to cultivation such as poor rains, pest attacks, etc., and to predict crop cultivation exactly. The main goal of the Maha Agri Tech Project is to push technology like Artificial Intelligence (AI) to forecast indicative crop yield or estimation through an accurate analysis of highly localized soil health conditions using satellite imagery technology.
Examples of farming activities with AI companies
- Weed control – Blue River Technology
- Harvesting and packaging – Harvest CROO Robotics
- Diagnosing pests and soil defects – PEAT
- Soil analysis – Trace Genomics
- Crop health monitoring – SkySquirrel Technologies
- Lettuce thinning – Blue River Technology
- Self-driving tractors – Autonomous Tractor Corp
- Weather, pests and disease prediction – aWhere
- Fruit picking robot- Tevel Aerobotics
Role of artificial intelligence in agriculture- recent invention 2022
- Disease detection and classification of Grapes and Tomatoes leaf with Deep-Learning (DL).
- Better model performance was achieved with increase in dataset volume.
- Performance metrics improved by setting the very high parameters tunings with the VGG model.
- CNN multilayer model precision of 98.40% of grapes and 95.71% of tomatoes.
- Proposed research delivering advancement in agriculture sectors & increasing production.
- An experiment to assess the economic advantage of two precision agriculture methods.
- The benefits of short and longer terms with both the single and integrated technologies.
Advantages of artificial intelligence in agriculture
- Market demands analysis.
- Efficient production.
- Risk assessment and management.
- Soil monitoring.
- Breeding and hybridity of seeds.
- Crop safety.
- Cost-efficient.
- Better decision-making.
- Automatic harvesting.
- Real-time data collection.
Disadvantages of artificial intelligence in agriculture
- The cost of AI-powered devices is too high.
- Maintenance
- Unemployment of farmers
- Robots can change the cultural appeal of agriculture.
- Energy cost and maintenance.
- The high cost of research and development.
- Lack of access to poor farmers.
Challenges of AI in agriculture
Though Artificial Intelligence in agriculture offers broad opportunities for farmers, few farmers fail to understand high tech machine learning solutions in farms across most parts of the world.The technology is much beyond the reach of most countries, there is also negative impact on the environment which might disturb the ecology. AI powered machines can only perform the programmed task, It cannot improvise or be creative in problem solving.
also read artificial intelligence in healthcare.
