The agricultural business has always been the most primitive and vital in the world, and it continues to be. Since humans found farming and moved from a wandering hunting-gathering lifestyle to one of the farmers, the need for food grains has risen.
The world’s population is expanding rapidly, and with it, the demand for food and employment is also growing. Our farmers’ conventional methods are insufficient to satisfy these needs. To make things easier and more productive, modern automated methods were introduced.
Table of Contents:
- Introduction
- Market Overview
- Challenges faced by the Agriculture Industry
- How AI can be Useful in Agriculture
- Applications of AI in Agriculture
- Future of AI in Agriculture
- The Final Say
Introduction:
AI in the agriculture sector may be used for a variety of technological advancements. Artificial intelligence consulting services, data analytics, the internet of things, and the usage of cameras and other sensors, for example, are included in this category of services. AI in agriculture will make better predictions by evaluating multiple data sources, such as weather, soil, crop performance, and temperature.
As a result of these AI-powered technologies, agriculture may produce better crops and enhance a wide range of agricultural-related operations along the whole food supply chain. All of these new technologies have contributed to increasing the demand for food and creating employment opportunities for billions of people throughout the system. AI in agriculture has made an agriculture revolution and has defended the crop yield from several factors like population growth, climate changes, employment issues, and food safety problems.
Also Read: An Entrepreneur’s Guide to AI in Your Business
Market Overview:
According to Markets and Markets, spending on AI technologies and solutions in Agriculture is estimated to grow from $1 billion in 2020 to $4 billion in 2026, attaining a Compound Annual Growth Rate (CAGR) of 25.5% between 2020 and 2026.
By region, North America generated the highest revenue in AI in the agriculture market, but it is predicted that the fastest growing market will be the Asia Pacific.
Artificial intelligence is deployed in agriculture, mainly in livestock and indoor farming, in 2019. Field farming is the primary farming type where AI is used in agriculture, with more than 60% market share.
Challenges faced by the Agriculture Industry:
Defined below are some of the key challenges that exist in the agricultural domain:
The decision-making process to prepare the soil, sow seeds, and harvest is becoming more challenging for farmers. Agriculture relies on a variety of climate conditions such as temperature, rainfall, and humidity. Deforestation and pollution are both contributing factors to climate change, which is a significant challenge for farmers.
Each crop requires a certain amount of soil nourishment. Phosphorus, potassium, and nitrogen are the three primary types of nutrients needed in the soil. Crop quality can be affected if one of these nutrients is missing.
Protecting plants and weeds is also crucial. If not controlled at the proper time, it can raise production costs and take nutrients from the soil, resulting in a nutritional shortage in the soil.
There are many potentials for agricultural applications, but most people are unfamiliar with the most recent technology.
How AI can be Useful in Agriculture:
Most of agriculture’s procedures and stages are done manually. AI can assist in the most complicated and routine tasks by complementing adopted technologies. It can gather and process big data on a digital platform, come up with the best course of action, and even initiate that action when combined with other technology.
The Role of AI in the Agriculture Information Management Cycle
Combining artificial intelligence and agriculture can be beneficial for the following processes:
- Analyzing Market Demand
Crop selection may be made easier with the help of artificial intelligence, which can assist farmers in determining which crops are most profitable.
- Managing Risk
Farmers can lower the risk of crop failures by using forecasting and predictive analytics.
- Breeding Seeds
Through collecting data on plant growth, AI can help produce crops that are less prone to disease and more suited to weather conditions.
- Monitoring Soil Health
AI systems can conduct chemical soil analyses and provide accurate estimates of missing nutrients.
- Protecting Crops
AI can monitor the health of plants to detect and even anticipate diseases, identify and eliminate weeds, and propose appropriate treatments.
- Feeding Crops
AI is useful for classifying optimal irrigation patterns and nutrient application times and predicting the optimal mix of agronomic products.
- Harvesting
With the help of AI, it’s possible to automate harvesting and even predict the best time for it.
Applications of AI in Agriculture:
Agriculture is the foundation of the world’s economy. In 2016, agricultural sectors contributed to just under 1% of the US GDP. The US Environmental Protection Agency(EPA) estimates that agriculture contributes around $330 billion annually. There is an estimation that the world will need to produce 50% more food by 2050 due to an increase in the population. Based on the research, the most popular applications of AI in agriculture fall into three major categories.
Agricultural Robots: Automated agricultural operations such as weed control, seed planting and harvesting, environmental monitoring and soil analysis are being developed by companies faster than humans.
Crop and Soil Monitoring: Data captured by drones or software-based technologies is being analyzed by computer vision and deep learning algorithms to identify potential flaws and nutrient deficiencies in the soil.
Predictive Analytics: Machine learning models have been created to track and predict diverse environmental influences on agricultural production, such as weather and climate change.
Future of AI in Agriculture:
As global population size increases, farmers now have to produce more food to feed a growing community, and the introduction of robotics and a digital workforce can offer automated assistance.
Genetically modified ingredients and food products promise customers access to fresh seasonal food year-round, which means farms have to depend on data to create longer seasons, bigger fields, or different grow times.
The future of artificial intelligence in agriculture will require a strong focus on universal access, as most cutting-edge technologies are now only used on large, well-connected farms. Increasing connectivity and outreach to even small farms in remote areas worldwide will cement the future of machine learning automated agricultural products and data science in farming.
The Final Say:
Technological advancements in agriculture will help the world deal with food production issues for the growing population. The growth in artificial intelligence technology has strengthened agro-based businesses to run more efficiently than ever. The time is now if you’re looking for an AI software development company for your amazing concept.
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