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Improving Crop Yield Analysis Using DBSCAN Clustering Algorithm.

Title: Improving Crop Yield Analysis Using DBSCAN Clustering Algorithm.


Agriculture is one of the most important sectors of the global economy, with millions of farmers working hard every day to produce enough food to feed the world's growing population. With the help of modern technology and data analytics, farmers and researchers can now gain valuable insights into crop yield and identify factors that impact crop production. In this case study, we will explore how DBSCAN clustering algorithm can be used to analyze agricultural data and improve crop yield analysis.

Data Preparation:

The agricultural data was collected from various countries and states, including data on districts and towns with information on food intake, soil fertility, temperature, and climatic conditions. To prepare the data for analysis, we broke down the data into towns based on their nature and conditions. We also identified multiple countries with similar temperature, climatic condition, soil fertility, and yields according to the type of crops they were cultivating.

DBSCAN Clustering Algorithm:

To cluster the data into different regions with similar climatic conditions and rainfall, we applied the DBSCAN clustering algorithm. The algorithm sorted the distance between each region based on their similarities, temperature range, and rainfall conditions, resulting in a set of clusters that can be considered for analysis. We then grouped the regions based on their soil type, which changed the clusters according to the sorted distance.

PAM Algorithm:

We also applied the PAM (Partitioning Around Medoids) algorithm to the dataset. By applying the number of clusters (k = 3), we categorized the crop yield into low, moderate, and high production. The districts, cities, and states were then clustered into 3 clusters using the PAM algorithm, resulting in a breakdown of various regions with different yields having the maximum and minimum production.

CLARA Algorithm:

In addition, we used the CLARA (Clustering Large Applications) algorithm to group the districts into 3 different clusters based on factors such as area, production, rainfall, and temperature. This helped us understand the relationship between temperature and crop production in different regions.

Multiple Linear Regression:

We then combined multiple linear regression with clustering algorithms like DBSCAN and PAM to gain a deeper understanding of the factors that affect crop production for different groups of crops. This helped us identify the most important factors that impact crop yields for different crops and make more informed decisions about crop management strategies. We used the P-value measure to test the significance of the relationship between the independent variables and dependent variables.


The results of our analysis showed that by using clustering algorithms like DBSCAN and PAM, farmers can identify which crops are most sensitive to changes in weather patterns and adjust their planting and harvesting schedules accordingly. The multiple linear regression equation for different crop yields, such as wheat, showed that a 1-unit increase in temperature level reduces the yield, while a 1-unit increase in rainfall or pH increases the yield. The optimal parameters for wheat crop production were determined using the DBSCAN, PAM, and CLARA clustering algorithms.


In conclusion, DBSCAN clustering algorithm can be a powerful tool for analyzing agricultural data and improving crop yield analysis. By using multiple clustering algorithms and combining them with multiple linear regression, farmers and researchers can gain a more nuanced understanding of the factors that impact crop yields for different groups of crops. This can help them make more informed decisions about crop management strategies tailored to specific clusters of crops, ultimately leading to increased crop yields and more sustainable agriculture.

At Yaganti Agrotech, we are committed to staying at the forefront of data analytics and leveraging the latest technologies to support the agricultural industry. Whether it's optimizing crop management strategies, improving supply chain efficiency, or enhancing research capabilities, we are always looking for new ways to innovate and support the seed industry with the power of technology.


1 comentário

Thakur Ranadheer singh
Thakur Ranadheer singh
18 de mai. de 2023


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