AI bots in the agriculture field can harvest crops at a higher volume and faster pace than human laborers. By leveraging computer vision helps to monitor the weed and spray them. Thus, Artificial Intelligence is helping farmers find more efficient ways to protect their crops from weeds.
“Implementation of artificial intelligence
(AI) for sustainable agriculture”
Pavan K. J and Prakash K. K
Department of Biotechnology
GM Institute of Technology, Davanagere, Karnataka, India- 577006
IQAC Initiated UGC -STRIDE Sponsored Two Days International e-Conference on
BRIDGING THE GAP BETWEEN ACADEMIA, RESEARCH & INDUSTRY FOR
LOCAL & GLOBAL COMPETENCY
Organized by Dept.of Biotechnology by KLE Society’s S. Nijalingappa College.
27th and 28th October 2020
Presentation By - Pavan K. J
Scope of AI in Agriculture
➢ According to UN Food and Agriculture Organization. The
population will increase to 10 billion by 2050.
➢ Double agricultural production in order to meet food
demands which is about 70% increase in food production.
➢ Only 4% additional land will come by 2050.
➢ Farm enterprises require new and innovative technologies to
face and overcome these challenges.
➢ By using AI we can resolve these challenges10/27/2020 2
➢ AI has a lot of direct application across sectors.
➢ AI can also bring a paradigm shift in farming.
➢ AI-powered solutions
Enable farmers to do more with less.
It will also improve quality and yield.
➢ Agriculture is seeing rapid adoption of AI both in
Agricultural products and
In-field farming techniques
HOW AI IS USED IN AGRICULTURE:
➢ Automated farming activities.
➢ Identification of pest and disease outbreak before
➢ Managing crop quality.
➢ Monitoring biotic.
➢ Abiotic factors and stress.
➢ Machine vision systems and phenotype lead to
➢ AI enabled drone in insect and pesticides spray
AUTOMATED IRRIGATION SYSTEM:
EFFECT OF USAGE:
Reducing production costs of vegetables, making the industry more
competitive and sustainable.
Maintaining (or increasing) average vegetable yields
Minimizing environmental impacts caused by excess
applied water and subsequent agrichemical leaching.
Maintaining a desired soil water range in the root zone that is optimal
for plant growth.
Low labor input for irrigation process maintenance
Substantial water saving compared to irrigation management based
on average historical weather conditions.
Barman A., Neogi B., Pal S. (2020) Solar-Powered Automated IoT-Based Drip Irrigation System. In: Pattnaik P., Kumar R., Pal S., Panda S. (eds) IoT and Analytics for
Agriculture. Studies in Big Data, vol 63. Springer, Singapore. https://doi.org/10.1007/978-981-13-9177-4_2
EFFECT OF USAGE
i)Reducing production costs of vegetables, making the industry more
competitive and sustainable.
ii)Maintaining (or increasing) average vegetable yields
iii)Minimizing environmental impacts caused by excess applied water and
subsequent agrichemical leaching.
iv)Maintaining a desired soil water range in the root zone that is optimal for
v)Low labor input for irrigation process maintenance
vi)Substantial water saving compared to irrigation management based on
average historical weather conditions.10/27/2020 7
AI - REMOTE SENSING: CROP HEALTH MONITORING:
➢ Image based Insight generation-
Use of Computer Visions
➢ Field Management:
Using high-definition images from
airborne systems (drone or copters),
real-time estimates can be made
during cultivation period by creating a
field map and identifying areas where
crops require water, fertilizer or
This helps in resource optimization to
a huge extent.
Image based Insight generation- Use of
Computer Visions Technology….
• Disease detection:
• Preprocessing of image ensure the
leaf images are segmented into areas
like background, non-diseased part
and diseased part.
• It also helps in pest
recognition and more.
Conventional methods are often time consuming and generally
categorical in contrast to what can be analyzed through automated
digital detection and analysis technologies categorized as remote
The trained use of hyperspectral imaging, spectroscopy and/or
3D mapping allows for the substantial increase in the number of
scalable physical observables in the field .
In effect, the multi sensor collection approach creates a virtual
world of phenotype data in which all the crop observables become
➢ Conventional methods are often time
consuming and generally categorical in
contrast to what can be analyzed through
automated digital detection and analysis
technologies categorized as remote
➢ The trained use of hyperspectral imaging,
spectroscopy and/or 3D mapping allows
for the substantial increase in the number
of scalable physical observables in the
➢ In effect, the multi sensor collection
approach creates a virtual world of
phenotype data in which all the crop
observables become mathematical values.
DECISION SUPPORT SYSTEM (DSS) FOR FIELD
PREDICTION USING AI TECHNIQUES
➢ This system involves a set of Artificial
Intelligence based techniques:
➢ Artificial Neural Networks (ANNs)
➢ Genetic Algorithms (GAs)
➢ Grey System Theory
➢ Use of artificial intelligence based
methods can offer a promising
approach to yield prediction and
compared favorably with traditional
AI -DRIVER LESS TRACTOR
Using ever-more sophisticated software coupled with off-the-shelf
technology including sensors, radar, and GPS, the system allows an
operator working a combine to set the course of a driverless tractor
pulling a grain cart, position the cart to receive the grain from the
combine, and then send the fully loaded cart to be unloaded.
AI FOR WEEDING
• The Hortibot is about 3-foot-by-3- foot, is self-propelled, and uses
global positioning system (GPS). It can recognize 25 different kinds
of weeds and eliminate them by using its weed- removing attachments
• HortiBotis eco-friendly,
because it sprays exactly
above the weeds
• As the machine is light --
between 200 and 300
kilograms --so it will not hurt
the soil behind it.
• It is also cheaper than the
tools currently used for weed-
elimination as it can work
during extended periods of
Drones are being used in agriculture
Precision fertilizer programme planning
Nitrogen deficient areas in a crop can be clearly identified from above using
drones fitted with cameras that have enhanced sensors.
Weed and disease control programmes
Using similar techniques to the fertilizer planning, drone operators can
accurately assess weed and disease levels in arable crops.
Tree and land mapping
As well as the disease control aspect, orchard fruit growers can benefit from
reports on tree and row spacing with accurate calculations of canopy coverage.
Larger drones are already capable of applying small quantities of pesticide or
fertilizer to crops, orchards and forested areas
Drone use in agriculture is growing as more farmers realise the technology’s
ability to perform key tasks and its fast-developing potential to take on bigger
roles in the future
AI can be appropriate and efficacious in agriculture sector as it
optimises the resource use and efficiency.
It solves the scarcity of resources and labour to a large extent.
Adoption of AI is quite useful in agriculture.
Artificial intelligence can be technological revolution and boom in
agriculture to feed the increasing human population of world.
Artificial intelligence will complement and challenge to make right
decision by farmers.