How Artificial Intelligence (AI) can help the Farmers?
There is a traditional proverb, “You Reap What You Sow”. But the thing that is missing in this proverb is, ‘Only if you’re Lucky’.
Agriculture involves so much risk that the farmers cannot talk about the yields. Whenever their crops get affected by any type of disease or when any Global pandemic hits, it is very hard to manage the various processes because most of them are not digital and that’s why we can say that the farmers can only get desired yields when everything is under control.
At the same time, the Global population is growing at a fast rate and urbanization is also continuously increasing. The disposable income is also rising and consumption habits are changing, that’s why the farmers are under a lot of pressure to meet the increasing demand, and therefore they need a way to increase the productivity of their farms.
If we look forward, then 30 years from now, there will be so much more people to feed and since the amount of fertile soil is also very limited there will also be a need to move beyond traditional farming. According to the UN Food and Agriculture Organisation, the population is going to increase by 2 billion by 2050 and only 4% of additional land will come under cultivation by then.
If we look at this context, only the use of the latest technological solution can make farming more efficient. Artificial Intelligence offers a lot of direct applications across various sectors and it can also bring a paradigm shift to how we see farming today.
The Artificial Intelligence (AI) powered solutions will not only help the farmers to do more with less but will also improve the quality and ensure a faster go-to-market for crops.
What is Artificial Intelligence (AI) ?
Artificial intelligence (AI) is a branch of computer science that simulates human Intelligence and it is implemented in machines to perform various tasks which require human intelligence. It combines computer science and Robust data sets to enable problem-solving.
What is the future of Agriculture & Farming?
Artificial Intelligence (AI) in Agriculture
Artificial intelligence is one of the technology-driven evolution in the agricultural industry which plays a very important role in transforming the Agro-Industry. It protects the agricultural sector from various factors such as climate change, population growth, employment issues, and food security.
It helps the agricultural industry with a wide range of Agricultural related tasks in producing healthy crops, controlling the pests, monitoring the soil and growing conditions, managing data for the farmers, and assisting the farmers with workloads, and with the entire food supply chain.
It has improved Crop production and Real-time monitoring, Harvesting, Processing, and Marketing, and the different Hi-Tech Computer-Based systems involved with AI have been designed to determine various important parameters such as weed detection, yield detection, crop quality, and many more.
Global Food Security is a challenge, we need to look for certain ways which could help the farmers minimize their risks and help them with more efficient farming methods or at least make them more manageable.
Implementing Artificial Intelligence (AI) in agriculture on a global scale can be one of the most promising opportunities as it can change the way of agriculture methods, as it enables the farmers to achieve more productive results with less effort, also bringing out many other benefits.
AI does not work independently but it is a supplement Technology to the already implemented Technologies so we can see it as a way from Traditional to Innovative farming.
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We need to understand that Artificial intelligence (AI) isn’t a panacea but it can bring tangible benefits to various small everyday things and it can simplify the lives of farmers in many ways.
Why is AI a challenge for Farmers?
The farmers perceive that AI can be applied only to the digital world and they do not believe that it can help them with their physical land as well because they lack understanding of the practical application of AI tools and they are unaware of the unknown.
These new technologies often seem confusing and they are unreasonably expensive because the AgriTech providers fail to explain why their solutions are useful and how they can be implemented and therefore Artificial Intelligence (AI) also faces the same situation in agriculture.
AI is useful but there is still a lot of work to be done by the technology providers to help the farmers implement it in the right way.
How Artificial Intelligence (AI) can be useful in Agriculture?
Agriculture involves a number of processes and stages and AI can facilitate the most complex and routine task by complementing the adopted technology. It can gather and process Big Data on a digital platform and can come up with the best course of action and can even initiate that action when it is combined with other technology.
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Some of the Agricultural processes where AI can be beneficial are:
- Analyzing Market Demand – AI can simplify the crops selection and it can help the farmers to identify which produce can be most profitable.
- Managing risk – The farmers can use the forecasting and predictive analytics so as to reduce the errors in business processes and therefore minimise the risk of Crop failures.
- Breeding seeds – By collecting the data on plant growth, AI can help in producing crops which are less prone to diseases and which can be better adapted to weather conditions.
- Monitoring soil health – The AI systems can conduct chemical soil analysis and can provide accurate estimates of the missing nutrients.
- Protecting crops – AI can monitor the plant and spot and predict the diseases, identify and remove weeds and can even predict effective treatment for those pests.
- Feeding crops – AI can be useful in identifying optimal irrigation patterns and nutrient application times and predicting the optical mix of agronomics products.
- Harvesting – AI can automate harvesting and can even predict the best time for harvesting the crops.
Scope of AI in Agriculture
Agriculture is rapidly adopting Artificial Intelligence and Machine learning, both in terms of agricultural products and in field farming techniques. Cognitive computing has been all set to become the most disruptive technology in the agricultural sector as it can understand, learn and respond to different situations, to increase efficiency.
How Technologies Changing Methods of Agriculture?
Here are the top 5 areas where the use of Cognitive solutions can benefit Agriculture.
- Growth driven by IoT – There is a huge volume of data that is generated everyday in both structured and unstructured format. These relate to the data on historical weather patterns, soil reports, research, rainfall, pest infestation, images from drones and cameras and so on. The cognitive solutions senses all these data and provide strong insight to improve the yield. There are two Technologies which are primarily used for intelligent data fusion which are – Proximity sensing and Remote Sensing. One example of this high resolution data is ‘Soil testing’. Remote sensing requires sensors to be built into airborne or satellite systems, while Proximity sensing requires sensors in contact with soil or at a very close range so it helps in soil characterization based on the soil below the surface in a particular place. These hardware solutions are paired with data collecting software with robotics to prepare the best fertilizer for growing the crops in addition to other activities for maximizing output.
- Image based insight generation – Precision farming is one of the most evolved technologies in farming today. The Drone based images help in in-depth field analysis, crop monitoring, scanning of fields and so on. The Computer Vision Technology, IoT and Drone data are combined to ensure rapid action by the farmers and the feeds from the Drone image data help in generating alerts in real time to accelerate Precision farming. Some of the areas where these Computer Vision Technology can be put into use are:
- Disease detection
- Crop readiness identification
- Field management
- Identification of optimal mix for agronomic products – There are multiple parameters like soil condition, weather forecast, type of seeds, infestation in a certain area and so on and based on these parameters, various cognitive solutions are recommended to the farmers on the best choice of crops and hybrid seeds. These recommendations can be further personalised based on the requirement of the farms, local conditions and data about successful farming in the past. There are some external factors like marketplace trends, prices or consumer needs which are also factored to enable the farmers to make a well informed decision.
- Health Monitoring of Crops – The Remote Sensing techniques along with hyperspectral image and 3D laser scanning is very important to build crop metrics across thousands of acres of land. This technique has the potential to bring a Revolutionary change in terms of how lands are monitored by farmers both from time and effort perspective. This technology will also be used to monitor the crops along their entire life cycle including report generation in case of anomalies.
- Automation techniques in irrigation and enabling farmers – Irrigation is a very important process in farming and it is human intensive. The AI machines are trained on historical weather patterns, soil quality and the kind of crops to be grown and it can automate irrigation and increase the overall yield. About 70% of the world’s freshwater is used in irrigation, so automation can help the farmers with their water problems and manage them in a better way.
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Applications of Artificial Intelligence in Agriculture
The traditional methods of agriculture have so many challenges for the farmers and to solve them Artificial Intelligence has become a Revolutionary technology which is helping the farmers in yielding healthier crops, controlling pests, monitoring soil and many more.
Some of the key applications of Artificial Intelligence in the agricultural sector are:
- Weather and Price Forecasting – The farmers face a lot of difficulties in taking right decision for harvesting, sowing the seeds and preparation of soil due to regular changes in climate but with the help of AI weather forecasting, they can get a detailed information on weather analysis, and accordingly, they can plan everything from soil preparation till the harvesting of the crop. Also, with Price forecasting, the farmers can get a better idea about the price of crops for the next few weeks and it can help them to get maximum profit.
- Health monitoring of crops – The quality of Crop mainly depends on the type of soil and its nutrition but due to deforestation and soil erosion, the quality of soil is degrading day by day and it is very hard to determine it. To resolve this issue, AI has developed an application called Plantix, which identifies the deficiency of soil including the pest and diseases and due to this the farmers can get an idea of using better fertilizer which can improve the quality of the crops. This app uses image recognition technology which can be used by the farmers to capture the images of plants and get information about the quality of the crop. The AI models can even alert the farmers to specific problem areas so that they can take immediate action.
- Agriculture Robotics – Robotics is widely used in various sectors mainly in manufacturing for performing Complex tasks but nowadays the AI companies have developed these robots to employ them in agriculture sectors and they are developed in such a way that they can perform multiple tasks in farming. These Robots are trained to check the quality of crops, detect and control weeds and harvest the crop with a faster speed as compared to humans.
- Intelligent spraying – The AI sensors can detect the weed easily and it also detects the affected areas. When these affected areas are detected, the herbicides can be precisely spread so as to reduce the use of herbicides and it also saves time and the crop. The robots developed by the AI companies help in spraying the herbicide on weeds and it widely reduces the number of chemicals to be used on fields. It allows the spraying of herbicides only on the affected (target) areas and not on the whole field, so it improves the quality of crops by preventing the use of herbicides and excessive toxins that make their way into our food and also saves money.
- Disease diagnosis – The farmers get knowledge of diseases very easily with the help of AI and they can easily diagnose the diseases with proper strategy and on time which help in saving the life of plant and farmers time. The Computer Vision Technology helps in getting the images of plants which can easily identify the diseased and non diseased parts and after detection, the diseased part is cropped and sent to the lab for further diagnosis. This technique also helps in the detection of pests, nutrient deficiency and many more.
How Agriculture Incorporates Technologies?
- Precision farming – Precision farming sums up as, “Right place, Right time and Right product”. This technique is very much accurate and a controlled way which can replace the labour intensive part of farming to perform repetitive tasks. It also provides guidance about crop rotation, optimum planting and harvesting time, water management, nutrient management best attacks and so on. Precision farming is the identification of stress levels in plants and it can be obtained using high resolution images and different sensor data installed on the plants. This data is then fed to a machine learning model as input for recognising the stress.
- Product Grading and Sorting – The AI computer vision can even assist the farmers after the harvest. As they help in detecting the plant defects, diseases, pests, so they can even be used to sort out good produce from the defective or ugly produce as well. It helps in inspecting the fruits and vegetables in terms of size, shape, colour and volume and the computer can automate the process of sorting and grading with accuracy and speed even more than a trained professional.
The future of farming largely depends on adoption of cognitive solutions. While large scale research is still in progress and some applications are already available in the market, the industry is highly undeserved and when it comes to handling realistic challenges faced by the farmers and using autonomous decision making and predictive solutions to solve them, farming is still at a nascent stage.