It tells about crop protection

1. Crop protection by early detection of insect
infestation
Team Members:
Name of the student USN
Mahesh 1MS20EE024
Shivayogi Halemani 1MS20EE051
Somashekhar Metri 1MS20EE054
Shrinivas Irakal 1MS21EE403
GROUP NO : 17
Under the guidance of Dr. R Subha
10/10/2023 DEPT.OF ELECTRICAL AND ELECTRONICS ENGINEERING 1

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Contents
 Introduction
 Problem Statement
 Objective
 Methodology
• Data collection
• Data processing
• Data augmentation
• Training of model
• Hardware deployment
 Work to be done
 References
 Thank you

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Introduction
The early detection of crop insects is an
important aspect of crop protection and can
help farmers minimize crop damage and
maximize crop yields.
About 20-25% of crop yield is lost due to insect
attack
 Every insect sound has a unique set of
frequencies which can be used to train the
Tiny ML model

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Problem Statement
Lack of early detection leads to the
following problems
Crop Damage and Loss.
Overuse of insecticides leads to soil
infertility
So the major problem is that a large
portion of a crop yield is lost due to insect
infestation which needs to be tackled using
modern technology

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Objective
The objectives of the project are as
follows:
To develop a ML model for early
detection of insect infestation using
audio signal
To deploy the model developed in
hardware
To test the working of the hardware
model for various insect sounds

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Methodology
Training of
model
Data collection
Data Processing
Data
augmentation
Hardware
deployment
Testing of
model

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Data Collection
We have collected the following insect sounds
to train the ML model
• AsianLongHornBeetle
• Bugcornfield
• Carpenterbee
• Cicada
• Locust
• Rootborer
• Stemborer Frequency Spectrum of cicada insect sound
• Weevil

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Data Processing
Preparation of dataset
 A Dataset comprising of audio files in a
format compatible for our ML model has
been created.
 The dataset contains audio files of
insect sounds of 3 seconds duration.
 All audio signals are sampled at 16 kHz
dataset to make the model more
accurate and to reduce the complexity.

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Data Processing
Creation of spectrogram
 We use spectrum of frequencies of a signal
as it varies with time.
 They can be used to identify specific features
of the audio signal, such as the presence of
certain frequencies or patterns over time.

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Data Processing
Splitting of dataset
The dataset is divided into 3 subsets
Each subset is used to train the following process
 Training (60%)
 Validating (20%)
 Testing (20%)

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Data Augmentation
Data augmentation is a set of techniques used to
increase the size of a dataset.
 Adding white noise to the audio samples
 Adding random silence to the audio
 Mixing two audio samples together

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Training of model
MaxPooling2D
layer
Conv2D layer Flatten layer

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Work to be done
Data augmentation
Model training with augmented data
Implementing on hardware
Testing of hardware

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References
 https://www.researchgate.net/publication/228923568_Acoustic_detection_and_automatic
_identification_of_insect_stages_activity_in_grain_bulks_by_noise_spectra_processing_t
hrough_classification_algorithms
 https://academic.oup.com/jee/article/113/6/3004/5910423
 https://www.sciencedirect.com/science/article/abs/pii/S0261219410000256
 https://www.nature.com/articles/s41598-022-06439-6
 https://www.nature.com/articles/s41598-022-06439-6

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Thank you