The document discusses the use of artificial intelligence in agriculture to optimize farming. It describes how AI can be used for tasks like crop readiness identification, field management, disease detection, and identifying the optimal mix of agronomic products. AI is also helping with tasks like irrigation automation through the use of drones and sensors to collect data. This data is then analyzed to monitor crop health and make recommendations to farmers to improve yields and farm efficiency through precision agriculture. The document provides several examples of how machine learning and computer vision are helping farmers make better decisions.

1. NAME: PRATHAMESH NAUKARKAR
SUBJECT:SEMINAR AND TECHNICAL COMMUNICATION
ROLL NO: 54
CLASS: T.E COMPUTER
TOPIC:USE OF ARTIFICIAL INTELLIGENCE IN AGRICULTURE FOR OPTIMISATION IN FARMING

2. Topic
ARTIFICIAL INTELLIGENCE IN
AGRICULTURE FOR OPTIMIZATION
IN FARMING

3. AI is a smart techniquemonitoringsystemto findsolutions easily
‘AI IS A ANALYTIC PROCESS ONE CAN ASSOCIATE
WITH HUMAN THINKING LIKE SPEECH RECOGNITION,
NATURAL LANGUAGE UNDERSTANDING AND
TRANSLATION ,KNOWLEDGE MANAGEMENT , IMAGE
ANALYSIS ,DECISION MAKING ,LEARNING ETC WHICH WILL
MAKE SYSTEMS POWERFUL AND USEFUL.’

4. Scope of AI in Agriculture
 Agriculture is witnessing rapid adoption of artificial intelligence (AI) and machine
learning (ML) in relation to both agricultural products and farming techniques in the
field. Cognitive computing, in particular, is becoming the most disruptive
technology for agricultural services due to its ability to understand, learn, and
(based on learning) become more efficient in a variety of situations.
 By making some of these solutions available to all farmers as services, such as
chatbots and other conversational platforms, farmers can keep up with
technological advances and apply the same to everyday farming. You can apply and
take advantage of this service.
 Microsoft is currently working with 175 farmers in Andhra Pradesh, India, to provide
seed, land and fertilizer advisory services. The initiative has resulted in an average
30% increase in yield per hectare compared to last year.

5. Tasks in agriculture done by using AI
 Crop readiness identification
 Field management
 Identificationof optimal mix for agronomic products
 Growth driven by IOT
 Disease detection
 Automation techniques in irrigationand enablingfarmers

6. Growth driven by IOT
 Huge amounts of data are generated every day in both structured and
unstructured formats. These relate to data on historical weather patterns, ground
reports, new research, rainfall, pest infestations, drone and camera images, and
more. A cognitive IOT solution can collect all this data and provide actionable
insights to improve yield.
 Proximity Sensing and Remote Sensing are two technologies primarily used for
Intelligent Data Fusion. One application for this high-resolution data is soil surveys.
Remote sensing requires the sensor to be embedded in an airborne or satellite
system, while proximity sensing requires the sensor to be in contact with the
ground or in very close proximity.
 This is useful for soil characterizationbased on the subsurface soil at a specific
location. Hardware solutions such as Rowbot (which refers to corn) combine data
collection software and robotics to prepare the best fertilizer for growing corn,
among other activities to maximize performance

7. Image-based insight generation
 Precision agriculture is one of the most debated areas in agriculture today. Drone-
based imagery is useful for detailed field analysis, crop monitoring, field scanning,
and more.
 Combining computer vision technology, IoT and drone data to help farmers act
faster.
 Feeds from drone imagery can generate real-time alerts to accelerate precision
agriculture. Here are some of the areas where computer vision technology can be
used

9. • Disease detection
 Image preprocessing ensures that leaf images are segmented into regions such as
background, non-pathological, and diseased areas.
 The diseased part is then cut out and sent to a remote laboratory for further
diagnosis. It also helps identify pests, detect nutritional deficiencies, etc.

10. Crop readiness identification
 Take images of different plants under white/UV-A light to determine how ripe the
green fruits .
 Farmers can create different preparationlevels for different crop/fruit categories
and add them to separate batches before sending them to market.

11. Field management
 Real-time estimations during the growing season by creating field maps and
identifying areas where plants need water, fertilizers, or pesticides using high-
resolution imagery from aerial systems (drones or helicopters) can be done.
 This is very useful for resource optimization

12. Identification of optimal mix for agronomic product
 Cognitive solutions recommend the best crop and hybrid seed choices for farmers
based on multiple parameters such as soil conditions, weather forecast, seed type
and prevalence in a particular region.
 Recommendations can be further personalized based on farm needs, local
conditions and data on past farming success.
 External factors such as market trends, prices and consumer needs can also be
taken into account to help farmers make informed decisions.

13. Health monitoring of crops
 In addition to hyperspectral imaging and 3D laser scanning, remote sensing
techniques are essential for creating harvest indicators across thousands of acres.
 Potential to revolutionize the way farmers monitor their fields in both time and
effort.
 This technology is also used to monitor the entire plant life cycle, including
generating reports if anomalies occur.

14. Automation techniques in irrigation and enabling farmers
 Real-time estimations during the growing season by creating field maps and
identifying areas where plants need water, fertilizers, or pesticides using high-
resolution imagery from aerial systems (drones or helicopters) can be done.
 This is very useful for resource optimization.
 Drones and helicopters play an important role in automation

15. Importance of Drone
 Prior to the harvest cycle, drones can be used to create 3D field maps with detailed
terrain, drainage, soil viability, and irrigation. Nitrogen levels can also be managed
with a drone solution.
 Aerial spraying of pods with seed and plant nutrients into the soil provides the
nutrients the plants need.
 Apart from that, drones can be programmed to spray liquids by adjusting the
distance to the ground based on technology. High-definition cameras and drones
collect precise field images that can be run through convolutional neural networks
to identify stress levels in weeds, water-hungry plants, and growing plants.
.

16. Precision Farming
 The phrase “right place, right time, right product” sums up precision agriculture. It
is a more precise and controlled technique that replaces the repetitive and labor-
intensive part of agriculture.
 It also provides guidance on crop rotation.
 Precision Positioning Systems
 Automated Guidance Systems
 Geomapping Sensors and Remote Sensing
 Integrated Electronic Communications Variable Rate Technology Optimal planting
and harvesting times, water management, nutrient management, pest infestation
and more.

17. Goals for precision farming
 Profitability: Strategically identify crops and markets and predict ROI based on
costs and margins.
 Efficiency: Investing in precisionalgorithms enables better, faster and cheaper
farming opportunities.
 This allows for overall accuracy and efficient use of resources
 Sustainability: Improved social, environmental and economic performance, with
incremental improvements in all performance indicators season after season

18. Examples of precision farming management
 Identification of plant stress levels is derived from high-resolution plant images
and multiple sensor data.
 This large dataset from multiple sources should be used as input to machine
learning to enable data fusion and feature detection for stress detection.
 A machine learning model trained on plant images can be used to detect plant
stress levels.
 The whole approach can be broken down into four phases: identification,
classification, quantification and prediction for better decision making.

19. Artificial Intelligence in Yeild management
 The emergence of new age technologies such as artificial intelligence (AI), cloud
machine learning, satellite imagery, and advanced analytics are building a smart
farming ecosystem.
 Integrating all of these techniques allows farmers to achieve higher average yields
and better price control. Microsoft is now working with farmers in Andhra Pradesh
to provide advisory services using the Cortana Intelligence Suite, which includes
machine learning and Power BI.
 The pilot uses his AI sowing app to recommend farmers sowing dates, tillage,
fertilization based on soil tests, farm fertilizer application, seed treatment, optimal
sowing depth, etc., and average yield per hectare. Increases volume by 30%.

20. Use of Robotics in digital farming