FarmingTechnology
Focusing on the strategic needs of modern agricultural development, Farming Technology applies the IOT technology and the data mining technology in big data to agricultural production, and builds a smart agricultural system to simulate the precise management and control
2025-06-28 16:27:13 - Adil Khan
FarmingTechnology
Project Area of Specialization Artificial IntelligenceProject SummaryFocusing on the strategic needs of modern agricultural development, Farming Technology
applies the IOT technology and the data mining technology in big data to agricultural
production, and builds a smart agricultural system to simulate the precise management and
control of agricultural greenhouses. Based on the in-depth analysis and thinking of smart
agriculture based on the IOT and big data technology, the K-means clustering algorithm
based on the maximum distance method to select the initial cluster center is proposed. And the
data storage, processing, and mining in the agricultural big data obtained with the IOT
technology are studied and optimized through the experimental simulation.
Farming Technology uses the IOT technology to collect a large amount of environmental
data from crop greenhouses, and uses the improved algorithm to select a relatively optimal
data as a clustering method for environmental reference data in the greenhouse in the next
cycle. The algorithm has good clustering effect and time performance.
? In farming climatic factors such as rainfall, temperature and humidity play an important
role in the agriculture lifecycle.
? Every crop requires specific nutrition in the soil. There are 3 main nutrients nitrogen (N),
phosphorous (P) and potassium (K) ,Soil Temperature & Moisture required in soil. The
deficiency of nutrients can lead to poor quality of crops.
? With a proper system, agriculture will be able to managed, planned and predict through
Big Data what is best suitable navigation in future.
The key parameters of Farming Technology are from Big Data + IOT + AI Algorithm for Recommender System.
Project ObjectivesDoes evaluation for farmers about farming methodologies and crops Through AI And Big
Data .
• Useful for growth production of crops By Live analysis Through Sensors.
• Gives farmer the required farming instinct In Recommender System Live 24/7.
• Helps farmers to farm healthy crops By Guidance through Data Set .
• Live monitoring and updating of Crop In system through specified Sensors .
• Helps to select best fertilizers Through System .
• Helps to maintain water level of crops as per required.
• Gives the right methods to farm the specific crop.
• Live Weather Updates.
Uses Big Data technology to give most accurate recommendations.
• 90% accurate prediction in Application thorough machine learning.
• Easy access to data because of cloud storage.
• Agriculture digitalization.
In farming climatic factors such as rainfall, temperature and humidity play an important
role in the agriculture lifecycle.
? Every crop requires specific nutrition in the soil. There are 3 main nutrients nitrogen (N),
phosphorous (P) and potassium (K) ,Soil Temperature & Moisture required in soil. The
deficiency of nutrients can lead to poor quality of crops.
? With a proper system, agriculture will be able to managed, planned and predict through
Big Data what is best suitable navigation in future.
? These are the key Parameter of Smart-Agro from Big Data + IOT + AI Algorithm By
recommender System.
? Cloud Database is utilized for storing the data which will be further used for predictions
in the recommender system Through Analyzed Graphical Representation.
?
The rise of IOT for the agricultural field has led to an alarming growth in the types and
quantities of agricultural data. Infrastructure of Big data technologies and cloud computing can
be applied to solve storage and analysis troubles of agriculture.
?
This recommender system shall enable the farmers to cultivate the crops and avoid all the
detrimental situations smartly by maximum usage of modern farming methods.
?
The right modern farming insight is given by this recommender system to cultivate
smartly and most adequately which will result in high yield production with minimum lose to the
producer.
Useful for growth production of crops By Live analysis Through Sensors.
• Gives farmer the required farming instinct In Recommender System Live 24/7.
• Helps farmers to farm healthy crops By Guidance through Data Set .
• Live monitoring and updating of Crop In system through specified Sensors .
• Helps to select best fertilizers Through System .
• Helps to maintain water level of crops as per required.
• Gives the right methods to farm the specific crop.
• Live Weather Updates.
Uses Big Data technology to give most accurate recommendations.
• 90% accurate prediction in Application thorough machine learning.
• Easy access to data because of cloud storage.
• Agriculture digitalization.
Selecting the initial clustering center randomly is easy to obtain the local
optimum in typical K-means algorithm, but difficult to get the global optimum
solution; different initial clustering centers are easy to get different clustering
results at the same time, which makes the clustering algorithm very unstable.
Selecting the initial cluster center randomly can raise the total amount of
iterations easily, thus increasing the cost of clustering in all.
Sample points with small similarity (large distance) are less to be divided into
the similar cluster, while those with large similarity are more likely to be
divided into the same cluster. Therefore, this computes the space between N
points in the sample set by the improved algorithm, which is as follows:
1. The two sample points with the farthest distance are taken as the initial
cluster centers.
2. The sample point in the remaining (N?–?2) sample points which maximizes
the space product of each of the first two initial clustering centers is selected
as the third initial clustering center.
3. Select the sample point in the remaining (N?–?3) sample points, the product
of the distance is the maximum value to the three initial cluster centers ahead
as the fourth initial cluster center
The center which maximizes the space product of each of the first three
initial cluster centers is selected as the fourth initial cluster center.
4. K initial cluster centers can be found by analogy.
The K-means algorithm based on the maximum distance are analyzed and
compared with the original K-means algorithm and the optimal partition K-means algorithm.
The improved algorithm is equivalent to selecting the cluster center selectively,
which is more purposeful than the K-means algorithm, so the number of iterations
is less.
Compared with the original K-means clustering technology, the improved Kmeans
clustering method reduces the total time consumption by 0.23?s on average and
increases the F measure by 7.67%. The above experimental results show that the
clustering effect and time performance of the improved algorithm is the best.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 34850 | |||
| DS18B20 Temperature Sensor | Equipment | 3 | 320 | 960 |
| SEAS-01 Soil Moisture Sensor | Equipment | 5 | 250 | 1250 |
| NRF24L01 2.4GHz Wireless Transceiver Module | Equipment | 3 | 130 | 390 |
| ESP32 Development Board ESP32 WiFi Bluetooth Development Board | Equipment | 3 | 10000 | 30000 |
| NPK Sensor | Equipment | 5 | 450 | 2250 |