Python大数据项目实战：大豆农业数据分析与Echarts可视化系统设计

前言

💖💖作者：计算机程序员小杨 💙💙个人简介：我是一名计算机相关专业的从业者，擅长Java、微信小程序、Python、Golang、安卓Android等多个IT方向。会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。热爱技术，喜欢钻研新工具和框架，也乐于通过代码解决实际问题，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💕💕文末获取源码联系 计算机程序员小杨 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目 计算机毕业设计选题 💜💜

一.开发工具简介

大数据框架：Hadoop+Spark（本次没用Hive，支持定制） 开发语言：Python+Java（两个版本都支持） 后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（两个版本都支持） 前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery 详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy 数据库：MySQL

二.系统内容简介

《基于大数据的高级大豆农业数据分析与可视化系统》是一个集成Hadoop分布式存储、Spark大数据处理引擎、Python数据分析和Vue前端可视化技术的综合性农业数据分析平台。系统采用Django作为后端框架，结合Pandas、NumPy等数据科学库进行大豆农业数据的深度挖掘和分析。通过HDFS分布式文件系统存储海量农业数据，利用Spark SQL进行高效的数据查询和处理，实现了从数据采集、清洗、分析到可视化展示的完整数据处理流程。系统核心功能包括可视化大屏展示、用户权限管理、大豆农业数据管理、核心基因性能分析、环境胁迫适应性评估、产量性状关联分析、综合性能优选评价以及农业数据特征统计等八大模块。前端采用Vue+ElementUI构建响应式界面，集成Echarts图表库实现多维度数据可视化，支持柱状图、折线图、散点图等多种图表类型，为农业研究人员和决策者提供直观的数据分析结果。整个系统架构设计合理，技术栈完整，能够有效处理大规模农业数据，为现代农业信息化发展提供技术支撑。

三.系统功能演示

Python大数据项目实战：大豆农业数据分析与Echarts可视化系统设计

四.系统界面展示

五.系统源码展示

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, avg, sum, count, max, min, when, desc, asc
from pyspark.sql.types import StructType, StructField, StringType, DoubleType, IntegerType
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views import View
import json

spark = SparkSession.builder.appName("SoybeanDataAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()

def core_gene_performance_analysis(request):
    gene_data = spark.read.option("header", "true").csv("hdfs://localhost:9000/soybean/gene_data.csv")
    gene_data = gene_data.withColumn("expression_level", col("expression_level").cast(DoubleType())).withColumn("protein_content", col("protein_content").cast(DoubleType())).withColumn("oil_content", col("oil_content").cast(DoubleType()))
    performance_stats = gene_data.groupBy("gene_type").agg(avg("expression_level").alias("avg_expression"), avg("protein_content").alias("avg_protein"), avg("oil_content").alias("avg_oil"), count("gene_id").alias("gene_count"))
    high_performance_genes = gene_data.filter((col("expression_level") > 75) & (col("protein_content") > 40)).select("gene_id", "gene_name", "expression_level", "protein_content", "oil_content")
    correlation_analysis = gene_data.select("expression_level", "protein_content", "oil_content").toPandas()
    correlation_matrix = correlation_analysis.corr()
    gene_ranking = gene_data.withColumn("performance_score", col("expression_level") * 0.4 + col("protein_content") * 0.35 + col("oil_content") * 0.25).orderBy(desc("performance_score")).limit(50)
    performance_distribution = gene_data.groupBy(when(col("expression_level") > 80, "优秀").when(col("expression_level") > 60, "良好").otherwise("一般").alias("performance_level")).count()
    functional_genes = gene_data.filter(col("gene_function").contains("产量")).select("gene_id", "gene_name", "expression_level", "gene_function")
    expression_variance = gene_data.groupBy("gene_type").agg(avg("expression_level").alias("mean_expr"), (sum(col("expression_level") * col("expression_level")) / count("expression_level") - (avg("expression_level") * avg("expression_level"))).alias("variance"))
    quality_metrics = gene_data.filter(col("protein_content") > 35).groupBy("variety_name").agg(avg("protein_content").alias("avg_protein"), max("protein_content").alias("max_protein"), min("protein_content").alias("min_protein"))
    gene_pathway_analysis = gene_data.filter(col("pathway_involvement").isNotNull()).groupBy("pathway_involvement").agg(count("gene_id").alias("gene_count"), avg("expression_level").alias("pathway_avg_expression"))
    result_data = {"performance_stats": performance_stats.toPandas().to_dict('records'), "high_performance_genes": high_performance_genes.toPandas().to_dict('records'), "correlation_matrix": correlation_matrix.to_dict(), "gene_ranking": gene_ranking.toPandas().to_dict('records'), "performance_distribution": performance_distribution.toPandas().to_dict('records'), "functional_genes": functional_genes.toPandas().to_dict('records'), "expression_variance": expression_variance.toPandas().to_dict('records'), "quality_metrics": quality_metrics.toPandas().to_dict('records'), "pathway_analysis": gene_pathway_analysis.toPandas().to_dict('records')}
    return JsonResponse({"status": "success", "data": result_data, "message": "核心基因性能分析完成"})

def environmental_stress_adaptation_analysis(request):
    stress_data = spark.read.option("header", "true").csv("hdfs://localhost:9000/soybean/stress_data.csv")
    stress_data = stress_data.withColumn("temperature", col("temperature").cast(DoubleType())).withColumn("humidity", col("humidity").cast(DoubleType())).withColumn("drought_index", col("drought_index").cast(DoubleType())).withColumn("survival_rate", col("survival_rate").cast(DoubleType())).withColumn("yield_reduction", col("yield_reduction").cast(DoubleType()))
    temperature_adaptation = stress_data.groupBy(when(col("temperature") > 35, "高温").when(col("temperature") > 25, "适温").otherwise("低温").alias("temp_category")).agg(avg("survival_rate").alias("avg_survival"), avg("yield_reduction").alias("avg_yield_loss"), count("sample_id").alias("sample_count"))
    drought_resistance = stress_data.filter(col("drought_index") > 0.5).groupBy("variety_name").agg(avg("survival_rate").alias("drought_survival"), avg("yield_reduction").alias("drought_yield_loss"), max("drought_index").alias("max_drought_tolerance"))
    stress_correlation = stress_data.select("temperature", "humidity", "drought_index", "survival_rate", "yield_reduction").toPandas()
    stress_corr_matrix = stress_correlation.corr()
    adaptation_score = stress_data.withColumn("adaptation_index", (col("survival_rate") * 0.6) - (col("yield_reduction") * 0.4)).orderBy(desc("adaptation_index")).limit(30)
    humidity_impact = stress_data.groupBy(when(col("humidity") > 70, "高湿").when(col("humidity") > 50, "中湿").otherwise("低湿").alias("humidity_level")).agg(avg("survival_rate").alias("humidity_survival"), avg("yield_reduction").alias("humidity_yield_impact"))
    multi_stress_tolerance = stress_data.filter((col("temperature") > 32) & (col("drought_index") > 0.4) & (col("humidity") < 60)).select("variety_name", "survival_rate", "yield_reduction", "temperature", "drought_index", "humidity")
    stress_severity_analysis = stress_data.withColumn("stress_level", when((col("temperature") > 35) | (col("drought_index") > 0.7), "严重胁迫").when((col("temperature") > 30) | (col("drought_index") > 0.4), "中度胁迫").otherwise("轻度胁迫")).groupBy("stress_level").agg(avg("survival_rate").alias("level_survival"), count("sample_id").alias("level_count"))
    variety_resilience = stress_data.groupBy("variety_name").agg(avg("survival_rate").alias("overall_survival"), avg("yield_reduction").alias("overall_yield_loss"), max("adaptation_index").alias("best_adaptation"))
    seasonal_stress_pattern = stress_data.filter(col("season").isNotNull()).groupBy("season").agg(avg("temperature").alias("season_temp"), avg("drought_index").alias("season_drought"), avg("survival_rate").alias("season_survival"))
    result_data = {"temperature_adaptation": temperature_adaptation.toPandas().to_dict('records'), "drought_resistance": drought_resistance.toPandas().to_dict('records'), "stress_correlation": stress_corr_matrix.to_dict(), "adaptation_score": adaptation_score.toPandas().to_dict('records'), "humidity_impact": humidity_impact.toPandas().to_dict('records'), "multi_stress_tolerance": multi_stress_tolerance.toPandas().to_dict('records'), "stress_severity": stress_severity_analysis.toPandas().to_dict('records'), "variety_resilience": variety_resilience.toPandas().to_dict('records'), "seasonal_pattern": seasonal_stress_pattern.toPandas().to_dict('records')}
    return JsonResponse({"status": "success", "data": result_data, "message": "环境胁迫适应分析完成"})

def yield_trait_association_analysis(request):
    yield_data = spark.read.option("header", "true").csv("hdfs://localhost:9000/soybean/yield_data.csv")
    yield_data = yield_data.withColumn("plant_height", col("plant_height").cast(DoubleType())).withColumn("pod_number", col("pod_number").cast(IntegerType())).withColumn("seed_weight", col("seed_weight").cast(DoubleType())).withColumn("yield_per_plant", col("yield_per_plant").cast(DoubleType())).withColumn("protein_content", col("protein_content").cast(DoubleType())).withColumn("oil_content", col("oil_content").cast(DoubleType()))
    trait_correlation = yield_data.select("plant_height", "pod_number", "seed_weight", "yield_per_plant", "protein_content", "oil_content").toPandas()
    correlation_matrix = trait_correlation.corr()
    yield_factors = yield_data.groupBy("variety_name").agg(avg("yield_per_plant").alias("avg_yield"), avg("plant_height").alias("avg_height"), avg("pod_number").alias("avg_pods"), avg("seed_weight").alias("avg_seed_weight"))
    high_yield_varieties = yield_data.filter(col("yield_per_plant") > 25).groupBy("variety_name").agg(avg("yield_per_plant").alias("high_yield_avg"), count("plant_id").alias("high_yield_count"), avg("protein_content").alias("protein_avg"))
    morphological_yield_relation = yield_data.withColumn("height_category", when(col("plant_height") > 80, "高秆").when(col("plant_height") > 60, "中秆").otherwise("矮秆")).groupBy("height_category").agg(avg("yield_per_plant").alias("category_yield"), avg("pod_number").alias("category_pods"))
    quality_yield_tradeoff = yield_data.select("yield_per_plant", "protein_content", "oil_content").filter((col("protein_content") > 0) & (col("oil_content") > 0))
    quality_correlation = quality_yield_tradeoff.toPandas().corr()
    pod_yield_analysis = yield_data.groupBy(when(col("pod_number") > 50, "多荚").when(col("pod_number") > 30, "中荚").otherwise("少荚").alias("pod_category")).agg(avg("yield_per_plant").alias("pod_yield"), avg("seed_weight").alias("pod_seed_weight"))
    comprehensive_trait_score = yield_data.withColumn("trait_score", (col("yield_per_plant") * 0.4) + (col("protein_content") * 0.25) + (col("oil_content") * 0.2) + (col("pod_number") * 0.15)).orderBy(desc("trait_score")).limit(40)
    yield_stability_analysis = yield_data.groupBy("variety_name").agg(avg("yield_per_plant").alias("mean_yield"), (sum(col("yield_per_plant") * col("yield_per_plant")) / count("yield_per_plant") - (avg("yield_per_plant") * avg("yield_per_plant"))).alias("yield_variance"))
    genetic_yield_potential = yield_data.filter(col("genetic_background").isNotNull()).groupBy("genetic_background").agg(avg("yield_per_plant").alias("genetic_yield"), max("yield_per_plant").alias("genetic_max_yield"))
    result_data = {"trait_correlation": correlation_matrix.to_dict(), "yield_factors": yield_factors.toPandas().to_dict('records'), "high_yield_varieties": high_yield_varieties.toPandas().to_dict('records'), "morphological_relation": morphological_yield_relation.toPandas().to_dict('records'), "quality_correlation": quality_correlation.to_dict(), "pod_analysis": pod_yield_analysis.toPandas().to_dict('records'), "comprehensive_score": comprehensive_trait_score.toPandas().to_dict('records'), "yield_stability": yield_stability_analysis.toPandas().to_dict('records'), "genetic_potential": genetic_yield_potential.toPandas().to_dict('records')}
    return JsonResponse({"status": "success", "data": result_data, "message": "产量性状关联分析完成"})

六.系统文档展示

结束

💛💛想说的话：感谢大家的关注与支持！ 💕💕文末获取源码联系 计算机程序员小杨 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目 计算机毕业设计选题 💜💜