2. 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.
3. 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.
4. 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.
5. HARDWARE AND SOFTWARE REQUIRMENTS
HARDWARE
SOFTWARE
Ram :- 8Gb & More
HDD:- 16Gb
Processor:- i3
Operating System: Windows
Language: Python
Tools: Pandas, Numpy ,Seaborn
,Pytorch ,Tensorflow,Flask ,Bootstrap
7. 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.
• Reduce Environmental Impact
8. Cost-Effectiveness of Soil fertility and plant diseases analyzer
• Improve yield
• Early decision making
• Output gains: The project's success should be measured by the improvements in soil fertility and plant health.
These gains can translate into increased crop yields, improved quality, and reduced losses. The economic value
of these gains will depend on market demand, crop prices, and other market factors.
• Externalities: The project may have indirect effects on the environment, human health, and social welfare. These
externalities should be considered in the cost-effectiveness analysis.
Cost – Effectiveness
9. 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.
10. 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.
