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Advanced Soybean Growth Predictor
Data Science and Analytics
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About
An expertly curated collection of information about soybean plants, designed primarily to support advanced agricultural research. This data was gathered by researchers at the College of Agriculture, University of Tikrit, in 2025 with the intent of helping global food production and improving farming methods. The dataset reveals plant responses across varying experimental conditions, offering a valuable resource for understanding the factors that dictate optimal soybean growth and yield. It functions as a key resource for unlocking plant secrets and future agricultural advancements.
Columns
The dataset contains data points corresponding to 13 distinct characteristics for each soybean plant record:
- Parameters: Encodes the specific experimental conditions applied, including genotype (G, 6 types), salicylic acid application level (C: 250 mg, 450 mg, or control), and water stress level (S: 5% or 70% field capacity).
- Plant Height (PH): Measurement of the plant's vertical growth.
- Number of Pods (NP): The count of seed holders per plant.
- Biological Weight (BW): The measured weight of the plant.
- Protein Percentage (PPE): The percentage of protein found within the seeds.
- Relative Water Content in Leaves (RWCL): The amount of water present inside the leaves.
- ChlorophyllA663 and Chlorophyllb649: Measurements related to the chlorophyll content, which is vital for the plant's ability to produce food.
- Weight of 300 Seeds (W3S): A metric reflecting seed size and density.
- Leaf Area Index (LAI): A measure related to the total leaf surface area.
- Number of Seeds per Pod (NSP): The average seed count within each pod.
- Sugars (Su): Content of sugars in the plant.
- Protein Content (PCO): General protein metric.
- Seed Yield per Unit Area (SYUA): The total seed productivity recorded.
Distribution
This agricultural dataset contains a large volume of plant measurements, structured into 55,450 rows and 13 columns. Each row represents data captured from one individual soybean plant. The associated data file, "Advanced Soybean new.csv," has a size of 4.95 MB. Data is anticipated to be updated on an annual basis.
Usage
The data product is extremely useful for generating predictive models and improving farm management. Potential applications include:
- Predicting how well soybean plants will perform under specific environmental and treatment conditions.
- Identifying the most effective strategies for cultivating healthy and highly productive soybeans.
- Developing artificial intelligence and machine learning tools to assist farmers in making data-driven decisions.
- Supporting academic studies in plant science, genetics, and agronomy.
Coverage
The underlying research for this data was carried out by professors and researchers affiliated with the College of Agriculture at the University of Tikrit. The data collection was completed by 2025. Experimental variables cover six genotypes of soybean plants, three levels of salicylic acid treatment, and two distinct levels of water stress.
License
CC0: Public Domain
Who Can Use It
The dataset is intended for a range of professionals focused on agricultural advancement:
- Scientists and Researchers: To study the effects of specific chemical and environmental stressors on crop physiology.
- Farmers and Agricultural Engineers: To inform planting decisions and optimise yield strategies.
- Data Analysts and Visualizers: To reveal hidden patterns and display correlations between variables like water stress and protein content.
- AI Developers: To train machine learning models for agricultural decision support systems.
Dataset Name Suggestions
- Advanced Soybean Growth Predictor
- Tikrit University Soybean Yield Data
- Salicylic Acid and Water Stress Soybean Study
- Agricultural Crop Performance Data
Attributes
Original Data Source: Advanced Soybean Growth Predictor
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