The fields had been freshly plowed. The furrows ran straight and deep. Yet, thousands of farmers across Andhra Pradesh (AP) and Karnataka waited to get a text message before they sowed the seeds. The SMS, which was delivered in Telugu and Kannada, their native languages, told them when to sow their groundnut crops.
In a few dozen villages in Telengana, Maharashtra and Madhya Pradesh, farmers are receiving automated voice calls that tell them whether their cotton crops are at risk of a pest attack, based on weather conditions and crop stage. Meanwhile in Karnataka, the state government can get price forecasts for essential commodities such as tur (split red gram) three months in advance for planning for the Minimum Support Price (MSP).
Welcome to digital agriculture, where technologies such as Artificial Intelligence (AI), Cloud Machine Learning, Satellite Imagery and advanced analytics are empowering small-holder farmers to increase their income through higher crop yield and greater price control.
AI-based sowing advisories lead to 30% higher yields
“Sowing date as such is very critical to ensure that farmers harvest a good crop. And if it fails, it results in loss as a lot of costs are incurred for seeds, as well as the fertilizer applications,” says Dr. Suhas P. Wani, Director, Asia Region, of the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT), a non-profit, non-political organization that conducts agricultural research for development in Asia and sub-Saharan Africa with a wide array of partners throughout the world.
Microsoft in collaboration with ICRISAT, developed an AI Sowing App powered by Microsoft Cortana Intelligence Suite including Machine Learning and Power BI. The app sends sowing advisories to participating farmers on the optimal date to sow. The best part – the farmers don’t need to install any sensors in their fields or incur any capital expenditure. All they need is a feature phone capable of receiving text messages.
Flashback to June 2016. While other farmers were busy sowing their crops in Devanakonda Mandal in Kurnool district in AP, G. Chinnavenkateswarlu, a farmer from Bairavanikunta village, decided to wait. Instead of sowing his groundnut crop during the first week of June, as traditional agricultural wisdom would have dictated, he chose to sow three weeks later, on June 25, based on an advisory he received in a text message.
Chinnavenkateswarlu was part of a pilot program that ICRISAT and Microsoft were running for 175 farmers in the state. The program sent farmers text messages on sowing advisories, such as the sowing date, land preparation, soil test based fertilizer application, and so on.
For centuries, farmers like Chinnavenkateswarlu had been using age-old methods to predict the right sowing date. Mostly, they’d choose to sow in early June to take advantage of the monsoon season, which typically lasted from June to August. But the changing weather patterns in the past decade have led to unpredictable monsoons, causing poor crop yields.
“I have three acres of land and sowed groundnut based on the sowing recommendations provided. My crops were harvested on October 28 last year, and the yield was about 1.35 ton per hectare. Advisories provided for land preparation, sowing, and need-based plant protection proved to be very useful to me,” says Chinnavenkateswarlu, who along with the 174 others achieved an average of 30% higher yield per hectare last year.
“Sowing date as such is very critical to ensure that farmers harvest a good crop. And if it fails, it results in loss as a lot of costs are incurred for seeds, as well as the fertilizer applications.”
– Dr. Suhas P. Wani, Director, Asia Region, ICRISAT
To calculate the crop-sowing period, historic climate data spanning over 30 years, from 1986 to 2015 for the Devanakonda area in Andhra Pradesh was analyzed using AI. To determine the optimal sowing period, the Moisture Adequacy Index (MAI) was calculated. MAI is the standardized measure used for assessing the degree of adequacy of rainfall and soil moisture to meet the potential water requirement of crops.
The real-time MAI is calculated from the daily rainfall recorded and reported by the Andhra Pradesh State Development Planning Society. The future MAI is calculated from weather forecasting models for the area provided by USA-based aWhere Inc. This data is then downscaled to build predictability, and guide farmers to pick the ideal sowing week, which in the pilot program was estimated to start from June 24 that year.
Ten sowing advisories were initiated and disseminated until the harvesting was completed. The advisories contained essential information including the optimal sowing date, soil test based fertilizer application, farm yard manure application, seed treatment, optimum sowing depth, and more. In tandem with the app, a personalized village advisory dashboard provided important insights into soil health, recommended fertilizer, and seven-day weather forecasts.
“Farmers who sowed in the first week of June got meager yields due to a long dry spell in August; while registered farmers who sowed in the last week of June and the first week of July and followed advisories got better yields and are out of loss,“ explains C Madhusudhana, President, Chaitanya Youth Association and Watershed Community Association of Devanakonda.
In 2017, the program was expanded to touch more than 3,000 farmers across the states of Andhra Pradesh and Karnataka during the Kharif crop cycle (rainy season) for a host of crops including groundnut, ragi, maize, rice and cotton, among others. The increase in yield ranged from 10% to 30% across crops.
Pest attack prediction enables farmers to plan
Microsoft is now taking AI in agriculture a step further. A collaboration with United Phosphorous (UPL), India’s largest producer of agrochemicals, led to the creation of the Pest Risk Prediction API that again leverages AI and machine learning to indicate in advance the risk of pest attack. Common pest attacks, such as Jassids, Thrips, Whitefly, and Aphids can pose serious damage to crops and impact crop yield. To help farmers take preventive action, the Pest Risk Prediction App, providing guidance on the probability of pest attacks was initiated.
“Our collaboration with Microsoft to create a Pest Risk Prediction API enables farmers to get predictive insights on the possibility of pest infestation. This empowers them to plan in advance, reducing crop loss due to pests and thereby helping them to double the farm income.”
– Vikram Shroff, Executive Director, UPL Limited
In the first phase, about 3,000 marginal farmers with less than five acres of land holding in 50 villages across in Telangana, Maharashtra and Madhya Pradesh are receiving automated voice calls for their cotton crops. The calls indicate the risk of pest attacks based on weather conditions and crop stage in addition to the sowing advisories. The risk classification is High, Medium and Low, specific for each district in each state.
“Our collaboration with Microsoft to create a Pest Risk Prediction API enables farmers to get predictive insights on the possibility of pest infestation. This empowers them to plan in advance, reducing crop loss due to pests and thereby helping them to double the farm income,” says Vikram Shroff, Executive Director, UPL Limited.
Price forecasting model for policy makers
Predictive analysis in agriculture is not limited to crop growing alone. The government of Karnataka will start using price forecasting for agricultural commodities, in addition to sowing advisories for farmers in the state. Commodity prices for items such as tur, of which Karnataka is the second largest producer, will be predicted three months in advance for major markets in the state.
At present, price forecasting for agricultural commodities using historical data and short-term arrivals is being used by the state government to protect farmers from price crash or shield population from high inflation. However, such accurate data collection is expensive and can be subject to tampering.
“We are certain that digital agriculture supported by advanced technology platforms will truly benefit farmers.”
– Dr. T.N. Prakash Kammardi, Chairman, KAPC, Government of Karnataka
Microsoft has developed a multivariate agricultural commodity price forecasting model to predict future commodity arrival and the corresponding prices. The model uses remote sensing data from geo-stationary satellite images to predict crop yields through every stage of farming.
This data along with other inputs such as historical sowing area, production, yield, weather, among other datasets, are used in an elastic-net framework to predict the timing of arrival of grains in the market as well as their quantum, which would determine their pricing.
“We are certain that digital agriculture supported by advanced technology platforms will truly benefit farmers. We believe that Microsoft’s technology will support these innovative experiments which will help us transform the lives of the farmers in our state,” says Dr. T.N. Prakash Kammardi, Chairman, Karnataka Agricultural Price Commission, Government of Karnataka.
The model currently being used to predict the prices of tur, is scalable, and time efficient and can be generalized to many other regions and crops.
AI in agriculture is just getting started
Shifting weather patterns such as increase in temperature, changes in precipitation levels, and ground water density, can affect farmers, especially those who are dependent on timely rains for their crops. Leveraging the cloud and AI to predict advisories for sowing, pest control and commodity pricing, is a major initiative towards creating increased income and providing stability for the agricultural community.
“Indian agriculture has been traditionally rain dependent and climate change has made farmers extremely vulnerable to crop loss. Insights from AI through the agriculture life cycle will help reduce uncertainty and risk in agriculture operations. Use of AI in agriculture can potentially transform the lives of millions of farmers in India and world over,” says Anil Bhansali, CVP C+E and Managing Director, Microsoft India (R&D) Pvt. Ltd.