AI in Agriculture

- Overview

Food is considered a basic human need and can be met through farming. Agriculture not only meets basic human needs but is seen as a source of global employment. Agriculture is considered to be the backbone of the economy and a source of employment in developing countries such as India. Agriculture accounts for 15.4% of India's GDP. Agricultural activities are broadly divided into three main areas: pre-harvest, harvest and post-harvest. 

Artificial Intelligence (AI) is not a technology that operates independently. As the next step from traditional farming to innovative farming, AI can complement technologies already implemented. AI has the potential to change the way we look at agriculture, enabling farmers to achieve more with less effort while bringing many other benefits. Advances in the field of machine learning can help improve agricultural yields. 

Machine learning is the current technology that enables farmers to minimize agricultural losses by providing rich recommendations and insights about crops. Machine learning is a branch of AI and computer science that deals with statistics and optimization and has countless applications. Its main goal is to create algorithms that automatically extract patterns from datasets. It can be used to predict the performance of a plant, including whether it is resistant or tolerant to biotic stresses such as pests and diseases caused by insects, nematodes, fungi or bacteria, as well as abiotic stresses such as cold, drought, salinity, or soil nutrient deficiencies.

Agribusinesses need to know that AI is not a panacea. However, it can bring tangible benefits to the little things in daily life and simplify farmers' lives in many ways. So how can we use AI for sustainable agriculture? What are the opportunities for AI in agriculture? How can AI help us meet existing challenges?

- How AI Can Play a Role in Agriculture

Agriculture involves many processes and stages, most of which are manual. By complementing the technologies employed, AI can facilitate the most complex and routine tasks. It can collect and process big data on digital platforms, suggest the best course of action, and even initiate that action when combined with other technologies. 

Combining AI with agriculture may benefit the following processes: 

• Analyze market needs - AI can simplify crop selection and help farmers identify the most profitable produce. 
• Manage risk - Farmers can use forecasting and predictive analytics to reduce errors in business processes and minimize the risk of crop failure. 
• Breeding seeds - By collecting data about plant growth, AI can help produce crops that are less prone to disease and better adapted to weather conditions. 
• Monitoring crop and soil health - AI systems can perform chemical soil analysis and accurately estimate missing nutrients. 
• Protect crops - AI can monitor the state of plants to detect and even predict disease, identify and remove weeds, and recommend effective pest treatments. 
• Feed crops - AI can be used to identify optimal irrigation patterns and nutrient application times, and predict the best combination of agronomic products. 
• Reward - With the help of AI, harvesting can be automated and even the best time to harvest can be predicted.

[AI-controlled Vertical Farms - Plenty]

- AI Adoption is a Big Challenge for Farmers

There is no doubt that crop yields, quality and labor practices are more efficient today than they were 500 or even 50 years ago. However, there are still major needs (and areas) for improvement. The global population is exploding, with 9.9 billion people expected to be on the planet by 2050, when demand for food is expected to soar 35% to 56%. 

Not to mention that climate change is making resources like water and arable land even more scarce. Fortunately, technology provides us with another solution: artificial intelligence. From crop and soil monitoring using computer vision to disease detection and predictive analytics, farming is entering a whole new phase of development—thanks to artificial intelligence. 

Farmers tend to see AI as something that is only applicable to the digital world. They may not see how it can help them work on physical land. This is not because they are conservative or wary of the unknown. Their resistance is due to a lack of understanding of the practical application of AI tools. 

New technologies often seem confusing and unreasonably expensive because agtech providers fail to clearly explain why their solutions are useful and how they should be implemented. This is what happens with AI in agriculture. While AI is useful, technology providers still have a lot of work to do to help farmers implement it the right way.

- Applications of AI and Computer Vision in Agriculture

From automated pest and plant disease detection to smart spraying and produce sorting – here's how computer vision is changing the food and agriculture sector. 

Here are some examples of the most promising AI technologies to transform the agricultural sector.

• Crop and Soil Monitoring
• Insect and Plant Disease Detection
• Livestock Health Monitoring
• Smart spraying
• Automatic weeding
• Aerial Surveying and Imaging
• Production Grading and Classification 
• The Future of AI in Agriculture: Farmers as AI Engineers?

[More to come ...]