This document discusses the use of machine learning algorithms for crop mapping and monitoring crop phenology. It begins with an introduction to machine learning in agriculture, noting its use in predicting crop yields and quality. It then describes the main types of machine learning: supervised, unsupervised, and reinforcement learning. The document outlines how each type works. It also discusses how machine learning is used for crop mapping, monitoring growth cycles, and detecting diseases or weeds. Advantages include digital farming, yield prediction, and livestock management. Challenges are also noted, such as needing large clean datasets to train algorithms effectively.

1. Machine learning algorithms for crop
mapping and crop phenology
BY: NITIN KUMAR

2. Titles
• Introduction
• Types of machine learning
• Machine learning in crop mapping
• Advantages
• Future scope and challenges
• Conclusion

3. Introduction
Agricultural machine learning, for instance, is
not a mysterious trick or magic, but a set of
well-defined models that collect specific data
and apply specific algorithms to achieve
expected results. ... In farming, they are used
to predict yield and quality of crops as well as
livestock production.

4. Types of machine learning
• Supervised Learning
• Reinforcement Learning
• Unsupervised Learning

5. Supervised Learning
• Supervised learning is the machine learning task of
learning a function that maps an input to an output
based on example input-output pairs.In supervised
learning, each example is a pair consisting of an input
object and a desired output value (also called
the supervisory signal).

6. Reinforcement Learning
• Reinforcement learning (RL) is an area of machine
learning concerned with how software agents ought to
take actions in an environment in order to maximize the
notion of cumulative reward. Reinforcement learning is
one of three basic machine learning paradigms,
alongside supervised learning and unsupervised
learning.

7. Unsupervised Learning
• Unsupervised learning is a type of machine learning
that looks for previously undetected patterns in a data
set with no pre-existing labels and with a minimum of
human supervision. In contrast to supervised learning
that usually makes use of human-labeled data,
unsupervised learning, also known as self-
organization allows for modeling of probability
densities over inputs

8. Machine learning in Crop mapping
Crop Classification and recognition is a very
important application of Remote Sensing. ...
Polygons as feature space was used as
training data sets based on the ground truth
data for crop classification using machine
learning techniques.

9. Machine learning is everywhere
throughout the whole growing and
harvesting cycle. It begins with a seed
being planted in the soil — from the soil
preparation, seeds breeding and water feed
measurement — and it ends when robots
pick up the harvest determining the
ripeness with the help of computer vision

10. How can machine learning helps in crop
mapping

11. Advantages
 DIGITAL FARMING
 YIELD PREDICTION AND QUALITY ASSESSMENT
 CROP DISEASE AND WEED DETECTION
 SPECIES MANAGEMENT
 FIELD CONDITION DETECTION
 LIVESTOCK MANAGEMENT

12. Future scope & Challenges
• Similar services are offered by some other companies
like PEAT, Earth Food and V Drone Agro, which use AI
to assess soil conditions over the cloud to help farmers.
• Although the use of AI is promising when it comes to
farming, the development of AI algorithms can be
challenging in an agricultural setting. The first and foremost
block is the requirement of large amounts of data,
particularly clean data to efficiently train the algorithms.

13. Conclusion
More specifically, five ML models were implemented
in the approaches on crop management, where the
most popular models were ANNs . For water
management in particular evapotranspiration
estimation, two ML models were implemented and
the most frequently implemented were ANNs.
Finally, in the soil management category, four ML
models were implemented, with the most popular
one again being the ANN model.

15. Thank you…