Artificial intelligence belongs to the development of computer systems able to act as the human mind, such as visual perception, speech recognition, decision making, and translation between the languages.
The predictions are made that the global population will reach about10 billion people in 2050, enhanced agriculture production to meet the food demands in need of the hour which is about the 70% increase in food production.
Farm enterprise needs new and advanced technologies to overcome these challenges. By using artificial intelligence we can overcome these demands. Just imagine what will happen if the farm is under the control of such machinery which acts like humans and store information like human accurately and efficiently.
Predictions are made on the stored information and calculations that what situations will be in the future, based on the system under which measurements are controlled. This is the future of modern agriculture in which everything is under the control of machines.
Humans can only work for some hours but machines have no fixed time to work. In another way, every human mind has not strong decision-making abilities that are the way everyone cannot make the correct decisions but artificial intelligence-based machines can better learn the environment and can make strong decisions.
The ways AI help farms in future
- Automated irrigation system:
The production cost of vegetables is reduced which makes the industry more sustainable. Average vegetable production is also maintained. Environmental impacts are reduced and low labor input is needed.
- Image-based insight generation:
Precision farming is one of the top discussed areas in today farming. Crop monitoring, in-depth field analysis, and scanning are the drone-based images.
Computer vision technology and drone-based data are put together to take immediate actions by farmers. In real-time, it can generate the alert to accelerate precision farming. Given are some of the areas in which computer vision technology can be implemented,
- Disease detection
- Crop readiness identification
- Field management
- Soil survey and mapping
- Optimal mix for agronomic products:
Taking into account multiple parameters like type of seeds, soil condition infection in certain areas, weather forecast and so on recommendations to the farmers on the best choice of the crops and hybrid seeds can be made by cognitive solutions.
Further classification of recommendations can be made based on farmer requirements, data about the successful conditions in the past and local conditions. External factors are also considered as trends in the market, consumer needs, and price.
- Monitoring health of crops:
Advance techniques like remote sensing along with the 3D laser scanning are helpful for crop metrics across thousands of acres. Revolutionary changes can bring how the lands from the perspective of time and efforts are monitored by the farmers. The entire lifecycle can also be monitored by using this technology according to generate reports if an anomaly occurs.
The phrase “Right Time, Right Place, Right Products” sums up precision farming. This is a more precise and controlled technique that helps monotonous and labor-intensive parts of farming. It also provides the complete guideline about water management, pest attacks, harvesting time, crop rotation and so on.
Goals for precision farming
- Profitability: The cost can be gained by identifying crops, Predicting ROI as well as market plans.
- Efficiency: Better, faster and cheaper farming opportunities can by utilized by investing in precision algorithms. This increases the overall usage and efficiency of resources.
- Sustainability: By increasing social, economic and environmental performance will certainly cause incremental improvements for all the performance indicators for each season.
AI startup in agriculture
- Prospera: it was founded in 2014. It provides the cloud-based solutions that calculated all the existing data like by the farmers like aerial image and soil sensor and so on.
- Blue river technology: It was invented in 2011. Next-generation agriculture equipment can be built by using artificial intelligence, robotics and computer vision to reduce chemicals and saves cost. Identification can be done by using a computer vision that treats each plant so that immediate action can be taken by smart machines.
- Farmbot: Farmbot is also invented in 2011. The purpose is to take farming at a different level by encouraging people to grow crops at their place. Using an open-source software system everything is taken care of by this physical bot ranging from seed plantation to weed detection and soil testing of plants.
Challenges to AI in agriculture
Although Artificial intelligence is providing a large number of opportunities for application in agriculture there is still a present lack of familiarity with the latest technology across most of the world. External factors are also the main reasons.
So what is the good solution while planning to harvest, maybe no best one due to external factors. A lot of data and precision predictions are also needed to train the machines. Due to vast agricultural land, structural data can be generated easily while temporal data is a challenge to get.
For Example, some data can only be acquired once in a year when the crops grow. Since the basic structure takes time to grow, so we require a large amount of time to build the machine learning models.
AI is efficient and suitable in the agriculture sector. The future only depends on how much adopts the techniques of machine learning. A large number of applications are available in the market and some application is still in progress but still, the agriculture industry is underprivileged.
The amount of data that is captured by the smart machines like drones and satellite will give the agriculture industry new capacity to recognize opportunities and predict the changes. The predictions are made that in the coming 5-10 years satellite machine vision applications will become more and more common in the agriculture industry.
It is also important that farmers are provided with the training to revolutionize the latest technology and continue to improve. This will prove the importance of machinery over the long trail. We suggest that the agriculture industry will continue to adopt smart machine technology and will continue to monitor that trend.
Authors: Farkhanda Athar, Zeeshan Athar, Tabinda Athar