Artificial Intelligence – The Future of Agriculture

Lots of evidence show that farmers are quickly moving to modernize the farming process. Artificial intelligence is being used in new and amazing ways to bring the process of food cultivation into the future. Artificial is contributing towards improving agricultural productivity. The most popular applications of AI in agriculture fall into three major categories:

1. Agricultural robots

Autonomous robots are developed and programmed to handle essential agricultural tasks such as harvesting higher volume crops at a faster pace than human laborers.  

• Pest and weed control

Automation and robotics help farmers find more efficient ways to protect their crops from weeds. A robot called See & Spray is developed by Blue River Technology which reportedly leverages computer vision to monitor and precisely spray weeds on cotton plants that helps prevent herbicide resistance. Farmers are quickly adopting new high-tech ways of protecting plants against various kinds of pests.

• Sowing the seeds

Artificial intelligence power is being applied to agricultural big data in order to make farming much more efficient to do all sorts of work like type of soil in which a seed will grow best, probable soil conditions, types of seeds, weather forecast etc

• Harvest robotics

Harvest technologies are something new that operate on the basis of machine vision and sensor fusion to “see” where harvest fruits and berries are.

Learn also: How Artificial Intelligence is Transforming The Healthcare Industry

2. Crop and soil monitoring

Deforestation and degradation of soil quality are significant threats to food security and have a negative impact on the economy. Deep learning applications are developed which identify nutrient deficiencies in the soil. Possible solutions like soil restoration tips and techniques are then provided to the users.

Today companies are leveraging AI technology to monitor crop health which aims to help users improve their crop yield and to reduce costs.

A technique known as transfer learning is adopted to teach the AI to recognize crop diseases and pest damage with 98% accuracy.

• Predictive analysis

Machine learning algorithms in connection with satellites are used to predict weather, analyze crop sustainability, and evaluate farms for the presence of diseases and pests. AI aims to detect diseases, pests, and poor plant nutrition on farms by analyzing agricultural data derived from images captured by satellites and drones. Users get an idea exactly where the fertilizer is needed and can reduce the amount of fertilizer used by nearly 40 percent.

3. Chatbots for farmers

Chatbots are conversational virtual assistants who automate interactions with the farmers assisting them with answers to their questions, giving advice and recommendations on specific farm problems.

Data collected at farms or on the field are generated by sensors or agricultural drones. Thus offer a wealth of information about soil, seeds, crops, costs, farm equipment or the use of water and fertilizer. Internet of Things Technologies and advanced analytics technologies help farmers analyze real-time data like weather, temperature, prices or GPS signals. These technologies provide insights on how to improve and increase yield, improve farm planning, make smarter decisions about the level of resources needed, when and where to distribute them in order to prevent waste.