To remain profitable in agriculture under the present condition every farmer should consider that fertility level must be measured, this presentation based on how recommendation gives based on soil testings.

1. Presented By:
Sandeep Wakchaure

2. Problem Statement
 What and Why :
This recommendation system is used to fill gap
between the famer and Agri experts.
 How :
By analyzing soil content and water minerals
available in field we are going to suggest a crop
and improving soil fertility which will provide
maximum benefit to farmer.

3. Background And Challenges
 Lack of awareness
 Currently soil testing center available at specific
villages only so rate of farmer uses this service is very
rare.
 Government provided online portal for this to use this
services but low ratio of literacy in farmer they not
able to rich up to this and take its benefits.
 In the existing system there is no any facility like
DoorStep
Service provided to the farmer.

4. Proposed Design

5. Motivation behind design
 To remain profitable in agriculture under the
present condition every farmer should consider
that fertility level must be measured.
 The results which concluded after the soil testing
can used to maintain fertility of soil to more
precisely achieve top production and quality
crops.

6. Database Design
-Farmer Name
-Farmer Address
-Sample ID(PK)
-Date of
submission
-Date of reports
Minor Nutrients
Sample ID(PK)
Iron
-Zink
-Copper
-Chlorine
-Boron
-Magnesium
Inputs from
Farmer
Analysis Report
and
recommendati
on based on
percentage rate
of content.
Major Nutrients
-Nitrogen(N)
-Phosphorus(P)
-potassium (K)
Secondary
Nutrients
-Sulfur(S)
-Calcium(C)
-Magnesium(Mg)
Content Analysis

7. Use Case Diagram

8. Algorithm And Explanation
 Collaborative filtering use data analysis technique
which is depends on likeness of score of the top n
item.
 where 'U' denotes the set of top 'N' users that are most
similar to user 'u' who rated item 'i'.

9. Algorithm And Explanation
 The user primarily based top-N
recommendation algorithmic program uses a
similarity-based vector model to spot the k most
similar users to a various user.

10. Sample Data Set
No Crop Water(mm/total growing period)
1 Cotton 700-1300
2 Citrus 900-1200
3 Maize 500-700
4 Beans 300-500
5 Sorghum 450-650
6 Sunflower 600-1000
7 Soybean 450-650
8 Wheat 1100-1500

11. Sample Data Set
Grain Time span in Days
Maize 80-110
Lentil 150-170
Grain 150-165
Cotton 180-195
Citrus 240-365
Dry 95-110
Bean 75-90
Wheat/Oats 120-150
Alfalfa 100-365
SunFlower 125-130
Squash 95-120
Soybean 135-150
Sorghum 120-130
Rice 90-150
Pepper 120-210
Dry 150-210
Onion 70-95
Millet 105-140
Melon 120-160

12. Sample Data Set
Element Range Classified
pH 7 to Above Alkaline
7 Nutral
Below 7.0 Acid
6.0 to 6.5 Best
P 0 to 10ppm Very low
20-30 ppm Medium
50 to Above Very high

13. Database Design of system

14. Conclusion
 A soil analysis and recommendation system is used to determine the
level of nutrients found in a soil sample.
 It can only be as accurate as the sample taken in a particular field from
farmer. The results of a soil analysis provide the agricultural producer
with an estimate of the amount of fertilizer nutrients needed to
supplement those in the soil for the specific crops or suggested crops .
where recommendation is based on soil content, water availability and
duration they like spend for the crop.
 Applying the appropriate type and amount of needed fertilizer will
give farmer in the agricultural a more reasonable chance to obtain the
desired crop yield.
 Therefore this system evaluate crop content and soil fertility status
and plan a nutrient management program. Finally This system gives
recommendations of crop, required fertilizer to stay more profitable in
agriculture.