Artificial Intelligence and Machine Learning in Agriculture
Agriculture is brisked up for the biggest revolution with evolving Artificial Intelligence and Machine Learning techniques
Ten years back if we would have used the word AI (Artificial intelligence) it would have sounded like a sci fi movie plot, but as we move forward it’s impacting our life more and more. AI is based on the human intelligence that can be defined in a way that a machine can easily mimic it and execute tasks, from the simplest to even more complex. The goals of artificial intelligence include learning, reasoning, and perception and Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Now that you’ve an idea of what AI is, let’s dive deep on how it makes an impact on our modern-day farming.
AI and ML are making a huge impact across industries. Every industry is looking to automate certain jobs through the use of intelligent machinery, Agriculture is no exception. Agriculture and farming are some of the oldest and most vital practices in the world. It plays a critical role in the world’s economy. Worldwide, agriculture is more than a $5 trillion industry. As the population is increasing due to which natural resources are becoming insufficient to continue the demand-supply chain. So, we need smarter and more efficient ways for sustainable agriculture. But just like every technological revolution in the world, its coin also has two sides, both full of impacts and challenges.
Everyday farmers, especially from developing countries will face huge challenges due to the century old traditional practices
When you sow a seed, there are many factors you keep in mind like climatic factors such as rainfall, temperature and humidity play a critical role. Rising deforestation and pollution result in climatic changes, so it’s difficult for farmers to take decisions to prepare the farm for transplantation, carry on the production activities, and harvest. In absence of any data from the field (historic and real time), its difficult for farmers to take decisions backed by data and they have to depend upon.
The traditional ways of irrigation (flood irrigation without monitoring soil moisture or understand the crop water requirement ), lack of access to agriculture research practices, bad soil management ( adding fertilizer without even knowing what it lacks ), limited market access, limited market price discovery and guess work based actions results in high cost of production and very low ROI.
While we look at those challenges, how AI and ML is ready to take the farmers one step closer to the erupting evolution
AI uses machine learning, deep learning, image processing, Wireless Sensor Network (WSN) technology, robotics, Internet of Things (IoT) technologies, and many more advanced tools to make solutions to agricultural problems.
AI and ML technologies are helping farmers and growers to generate more yields by selecting appropriate crop varieties, adopting better practices in soil and nutrient management, pest and disease management, and helping in determining crop production estimates and predicting commodity prices.
Not just that, it makes a farmer’s life easier and works as a third helping hand in each process, as it bridges the gap between agriculture and technology. AI helps farmers to monitor real-time data such as weather, temperature, water usage, or soil conditions obtained from farms. Artificial intelligence can establish smart farming practices to reduce farmers’ losses, provide them with high yields and make well-informed decisions.
In today’s word smart farming is not just a cool word, it’s a useful tool that is ready to change the face of agriculture. The mechanism that drives smart farming is Machine Learning, which is a subset of AI. ML tools analyze, understand, and identify a pattern in agricultural operational environments.
If we talk about the state-of-the-art use cases of this modern technology, they are
· Map and estimate yields
· Irrigation planning
· Meet demand and eliminate waste
· Make smarter harvesting and pricing decisions
· Identify and remove harmful weeds
· Disease forecast
· Identify and Treat crop disease with targeted solutions
· Increase productivity, save time and operate more economical
How does Yuktix bridges the gap between traditional and modern farming with AI.
Yuktix GidaBits is an Agri intelligence software that provide 24x7 farm monitoring and Agri-advisory to farmers assisted by data captured by IoT devices, data analytics and ML algorithms.
AI and ML algorithms have become a critical part of Gidabits. Any AI or ML algorithms would need data to train and then real time data to work upon. With more than 2 million data points collected over the last 3 years from various agriculture devices deployed in the field, Yuktix has developed
- Short term rainfall prediction
- Disease forecasting models for tea-estates.
- Image based disease recognition (work in progress)
But how does Yuktix brings the entire process to life?
Here is a use case for you to understand.
Yuktix Agri-Intelligence for Tea-Estate – Yuktix IoT infrastructure allow us to deploy IoT devices in much more granular manner as compare to others. Yuktix weather station collect information about the micro-weather at tea-estate level like Rainfall, Temperature, Pressure, Wind speed, Wind direction, Solar Radiation, leaf wetness. They are assisted by smaller devices GreenSense which provide hill level Temperature, humidity, soil moisture and leaf wetness variation.
Once the data collected by these devices is pushed to Yuktix ankiDB IoT cloud via a solar gateway (blog - http://blog.yuktix.com/2020/04/digital-assistant-for-tea-estates-how_27.html) where it is processed and passed down to different processing pipelines. The data collected over the period of time is utilized to develop and train new models for red mite and tea blister blight disease forecast with a accuracy of 75%. With more data coming in and with feedback from the field staff, we plan to improve the accuracy of prediction up to 85% in next 1 year.
This has helped tea-estate save upto 25% on the spray used in a season and reduce the losses upto 15%.
While as exciting AI/ML sound, the implementation is tougher than that.
The farmer’s reservation to adopt the smart farming techniques is again a big problem. The lack of education amongst growers becomes a block in the way of implementing AI. AI-based robots/devices cost a lot of money in research and development and they also need maintenance to keep them running smoothly. Most of the farmers in India have very small landholdings and are less resourceful. For the broader adoption, AI needs to be applicable, reasonable, available, achievable, and sustainable.
While at YuktiX we aim to push our boundaries to bring Indian farmers closer to the future of farming, it won’t be wrong to say that even if the destination is far way, the gap will be filled fast with modern technologies like AI and ML
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