AI
and Agriculture
How can AI help in agriculture?
Agricultural AI yields better crops through data analytics. The use of agricultural AI optimizes
the farming industry by decreasing workloads, analyzing harvesting data and
improving accuracy through seasonal forecasting.
Agriculture
has constantly improved with the introduction of various technologies, from
motorized equipment to biotechnology. Following business trends, the
agricultural industry is looking to maximize efficiency by turning to AI
technologies. AI technology has been implemented to help yield healthier crops,
reduce workloads, organize data and improve a wide range of tasks in this $5
trillion industry.
A modest
estimate puts 75 million IoT
agricultural devices in use by 2020 and 4.1 million daily data points
per farm by 2050. The volume of data -- collected through technologies ranging
from farm machinery to drone imagery -- is too abundant for humans to process.
Farmers and agricultural technology workers are turning to AI to help analyze
data points, thus enhancing value derived from these data sources.
Farms
produce hundreds of thousands of data points on the ground each day. With the
implementation of agricultural AI, farmers can now analyze weather conditions,
temperature, water usage or soil conditions collected from their farm to inform
decisions. AI technologies are helping determine the feasible crop choices or
which hybrid seeds will increase profit and decrease waste.
In
addition to ground data, computer vision and deep learning algorithms process data captured
from drones and unmanned aircraft systems. AI combined with unmanned aircraft
systems and drones can capture images of the entire farm and analyze the images
in near-real time -- monitoring and analyzing soil health and condition of
crops across the entire farm, as well as identifying problem areas.
Can this save money, improve lifestyle, help agriculturists / farmers?
AI
reduces farm workload.
AI technology can take
over planting, maintaining, harvesting crops and detect certain diseases in
plants and, thus, help
save money, energy, labour and resources.
The global use of AI for
agriculture is quite impressive.
Traditionally,
farms have needed many workers -- mostly seasonal -- to produce and harvest
crops to stay productive. However, less people are entering the farming profession, due to the physical labor and high turnover rate of
the job. Furthermore, most agricultural work utilizes a highly mobile migrant
workforce, which presents challenges for a stable and predictable workforce.
AI
solves critical farm labor challenges by augmenting or removing work and
reducing the need for large numbers of workers. Agricultural AI bots are
harvesting crops at a higher volume and faster pace than human laborers, more
accurately identifying and eliminating weeds and reducing cost and risk. AI
farmers present a permanent solution for the unpredictable and fluctuating
agricultural workforce.
Additionally,
farmers are taking advantage of chatbots for
assistance to seek advice and recommendations on specific problems. Chatbots
are already being used in numerous industries with great success, so it's no
surprise that AI-powered chatbots should help farmers as well. Through the use
of agricultural AI and cognitive technologies, farms across the world are able
to run more efficiently to produce the fundamental staples of our dietary
lifestyles.
What
are the expectations of AI in Agriculture?
From the
analysis of soil to planting to harvesting, with quick decision making in the
face of problems, Artificial Intelligence is present in applications related
to:
·
Greater
accuracy in the identification of climate phenomena influencing the crop, such
as temperature, precipitation, direction and wind velocity practically in real
time, which makes data analysis very rich.
·
-Complete
mapping of rural properties, helping to identify, for example, deforestation,
overuse of pesticides and pests.
·
Orientation
to the producer on which agricultural input to use, at what time and in what
quantity so that he can achieve maximum productivity on his farm.
Agriculture is slowly
becoming digital and AI in agriculture is emerging in three major categories,
(i)
Agricultural
robotics
(ii)
Soil and crop
monitoring
(iii)
Predictive
analytics.
Farmers are increasingly
using sensors and soil sampling to gather data and this data is stored on farm
management systems that allows for better processing and analysis. The
availability of this data and other related data is paving a way to deploy AI
in agriculture.
We are seeing as a result
a number of tech companies investing in algorithms that are becoming useful in
agriculture. For example we have image recognition used in potatoes, AgVoice by a Georgia-based startup for using natural
language toolkit for field notes, and yield prediction algorithms
based on satellite imagery.
Has
it already been implemented?
Digital Agriculture: Farmers in India are using AI to
increase crop yields. 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).
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.
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