BE-IT

1. Soil fertility and Plant disease
analyzer
GROUP NO : 17
Guided By: Prof. Sonali Padalkar
Group members:-
Aadarsh Mishra (25)
Nilesh Gahlot (10)
Sandip Yadav (70)
Shivamkumar Prasad (44)

2. Abstract
Agriculture is an essential part of human lives. It is one of the major source of employment in India. More than half of
the population depend upon agriculture. It is the backbone of our economy. Crop yield depends on many factors. The
major factors which affect the yield of the crop is soil and plant diseases .The early detection of diseases is important in
agriculture for an efficient crop yield. The bacterial spot, late blight, septoria leaf spot and yellow curved leaf diseases
affect the crop quality of plants. Automatic methods for classification of plant diseases also help taking action after
detecting the symptoms of leaf diseases. Improvising the techniques to predict crop yield in different climatic conditions
can help farmers and other stakeholders in better decision making in terms of agronomy and crop selection. Crop yield
prediction includes forecasting the yield of the crop from previous historical data which consists of factors such as
temperature, humidity, pH, rainfall and crop name.
Keyword - CNN, Agriculture

3. NeedIdentification
• Agriculture is one of the main occupation in India. Large population of India depends upon agriculture as their main source
of income. With time, the demand for production has been increased exponentially.
• The availability of accurate and timely information such as soil, meteorological, usage of fertilizers, usage of pesticides can
help farmers to make accurate decision as per their need for their cropping situations . This can assist them to achieve
greater crop productivity if the conditions are suitable or help them to reduce the loss due to un-favourable conditions for
the crop yield.
• If proper soil analysis is done , crops will grow time to time and in proper proportion but crops may get affected by bacteria
or fungus and so to avoid such situation plant disease detection is also needed.
• Advances in artificial intelligence researches now make it possible to make automatic plant disease detection from raw
images of plants.

4. PROBLEM DEFINATION
Indian agriculture sector employs nearly half of the country's workforce. India is the largest producer of pulses, rice, wheat,
spices, and spice products in the world. Farmers' economic growth is determined by the quality of the goods they make, which is
dependent on plant growth and yield. As a result, in the field of agriculture, soil specification for plants and disease identification
in plants is important. Plants are highly susceptible to diseases that inhibit plant development, which has an effect on the farmer's
ecology. The use of an automated disease detection technique is advantageous in detecting a plant disease at an early stage. Plant
diseases manifest themselves in various parts of the plant, such as the leaves. It takes a long time to manually diagnose plant
disease using leaf photographs. As a result, computational methods must be developed to automate the process of disease detection
and classification using leaf symptoms , and give them proper treatment.

5. SCOPE OF PROJECT
 Soil fertility is the ability of a soil to sustain plant growth by providing essential plant nutrients and favorable chemical,
physical, and biological characteristics as a habitat for plant growth.
 Identifying of the plants diseases in early stage is essential in prevention of yield and volume losses in agriculture Product.

6. COMPARATIVESTUDY
Parameter
Random
Forest
Logistic
Regression
SVM Classifier Naïve Bayes CNN
Method used
It uses
bagging
features
sigmoid function kernel trick Bayes' Theorem
with an assumption
of independence
among predictors
it is one of the
variants of
artificial neural
networks and is
widely used for
classification,
image processing.
Accuracy 0.99 0.95 0.97 0.99 0.97
Complexity Simple Very High Moderate Very High Moderate
Speed Fastest Slow Moderate Slow Fast
Advantage &
Disadvantage
Adv - Fast training.
Disadv –Heavy
Power
consumption.
Adv - much
easier to set up
and train
Disadv –Less
accuracy
Adv - gives you
distance to the
boundary
Disadv –less
accuracy than
random forest
Adv – lower
computational
time
Disadv –Slower
speed
Adv -
automatically
detect important
features
Disadv -
Massive Data
Requirement

7. PROPOSED SOLUTION
The proposed system involves two phases the training and testing phase. It uses two database the soil database and
crop database. The soil database includes the chemical features and geographical features of the soil. The proposed model is
based on soil and crop database. Several machine learning algorithms are used to classify
the soil type. For a particular soil type suitable crop is suggested. From the experimental result
Proposed block diagram for Plant
disease detection model:-
Proposed block diagram for crop yield prediction:-

8. System Flowchart

9. APPROCH : Random Forest
Random forest is a Supervised Machine Learning Algorithm that is used widely in Classification and Regression
problems. It builds decision trees on different samples and takes their majority vote for classification and average in
case of regression. One of the most important features of the Random Forest Algorithm is that it can handle the data set
containing continuous variables as in the case of regression and categorical variables as in the case of classification. It
performs better results for classification problems

10. System Interface

11. social impact
• Fertile soils contribute to food security, good yields for farmers and economic development
for the countries.
• Soil fertility affects economic development in many ways. Agriculture is often a key sector in
developing countries, and soil fertility is a key determinant of the success of this sector
• The earlier detection of disease may lead to more cures or longer survival.

12. CONCLUSION & FUTUREWORK
 An application of classifying the soil types and providing the necessary crop suggestions for the classified soil
series. The proposed work will benefit farmers to maximize productivity in agriculture, reduce soil degradation
in cultivated fields, and reduce fertilizer use in crop production by recommending the right crop considering
various attributes. The proposed work aids framers to accurately select the crop for cultivation and attain
sustainability.
 Our future work is aimed at an improved data set with large number of attributes and also implement Android
application version.

13. REFERENCES
[1] Pranay Malik , Sushmita Sengupta , Jitendra Singh Jadon “Comparative Analysis of Soil
Properties to Predict Fertility and Crop Yield using Machine Learning Algorithms “, 2021.
[2] Melike Sardogan, Adem Tuncer, Yunus Ozen “Plant Leaf Disease Detection and Classification
Based on CNN with LVQ Algorithm”, 2020.
[3] Kiran Moraye, Aruna Pavate, Suyog Nikam and Smit Thakkar” Crop Yield Prediction Using
Random Forest Algorithm for Major Cities in Maharashtra State “, 2020.
[4] Monali Paul, Santosh K. Vishwakarma, Ashok Verma” Analysis of Soil Behaviour and
Prediction of Crop Yield using Data Mining Approach” 2019.
[5] Dharesh Vadalia, Minal Vaity, Krutika Tawate, Dynaneshwar Kapse” Real Time soil fertility
analyzer and crop prediction”, 2019.

14. THANKYOU!