💖💖作者：计算机编程小咖 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常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。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，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！ 💜💜 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
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高级大豆农业数据分析与可视化系统介绍
《基于大数据的高级大豆农业数据分析与可视化系统》是一套专门针对大豆农业数据进行深度分析和直观展示的综合性平台，该系统充分运用现代大数据技术栈，通过Hadoop分布式存储框架和Spark内存计算引擎构建强大的数据处理底层架构，实现对海量大豆农业数据的高效存储、快速处理和实时分析。系统采用Python和Java双语言开发模式，后端分别支持Django和Spring Boot框架，前端基于Vue.js结合ElementUI组件库构建用户界面，并集成Echarts图表库实现丰富的数据可视化效果。系统核心功能涵盖核心基因性能分析、环境胁迫适应分析、产量形状关联分析、综合性能优选分析以及农业数据特征分析等多个专业分析模块，通过对大豆品种的基因特性、环境适应能力、产量表现等多维度数据进行深入挖掘，为农业科研人员和种植户提供科学的决策支持。系统还配备可视化系统大屏功能，能够将复杂的农业数据以直观的图表形式展现，同时具备完善的用户管理体系，包括个人信息管理、密码修改、系统公告等基础功能，整体架构采用MySQL数据库存储结构化数据，结合Pandas和NumPy等数据科学库进行数据预处理和统计分析，为现代农业的智能化发展提供了强有力的技术支撑。
高级大豆农业数据分析与可视化系统演示视频
高级大豆农业数据分析与可视化系统演示图片
高级大豆农业数据分析与可视化系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, avg, sum, count, when, desc, asc, round as spark_round
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.stat import Correlation
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views.decorators.http import require_http_methods
import json
spark = SparkSession.builder.appName("SoybeanAgricultureAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
@require_http_methods(["POST"])
def core_gene_performance_analysis(request):
data = json.loads(request.body)
gene_type = data.get('gene_type')
analysis_period = data.get('analysis_period')
soybean_df = spark.read.option("header", "true").option("inferSchema", "true").csv("/data/soybean_gene_data.csv")
filtered_df = soybean_df.filter(col("gene_type") == gene_type).filter(col("collection_date").contains(analysis_period))
performance_stats = filtered_df.groupBy("gene_variety").agg(
avg("protein_content").alias("avg_protein"),
avg("oil_content").alias("avg_oil"),
avg("yield_per_plant").alias("avg_yield"),
count("sample_id").alias("sample_count"),
spark_round(avg("disease_resistance"), 2).alias("avg_resistance")
)
top_performers = performance_stats.filter(col("sample_count") >= 10).orderBy(desc("avg_yield"))
correlation_data = filtered_df.select("protein_content", "oil_content", "yield_per_plant", "disease_resistance")
assembler = VectorAssembler(inputCols=correlation_data.columns, outputCol="features")
vector_df = assembler.transform(correlation_data)
correlation_matrix = Correlation.corr(vector_df, "features").head()[0].toArray()
gene_efficiency = filtered_df.withColumn("efficiency_score",
(col("protein_content") * 0.3 + col("oil_content") * 0.25 + col("yield_per_plant") * 0.35 + col("disease_resistance") * 0.1))
efficiency_ranking = gene_efficiency.groupBy("gene_variety").agg(
avg("efficiency_score").alias("avg_efficiency"),
spark_round(avg("growth_cycle"), 1).alias("avg_growth_days")
).orderBy(desc("avg_efficiency"))
result_data = {
'performance_stats': [row.asDict() for row in top_performers.collect()],
'correlation_matrix': correlation_matrix.tolist(),
'efficiency_ranking': [row.asDict() for row in efficiency_ranking.limit(20).collect()],
'total_varieties': filtered_df.select("gene_variety").distinct().count(),
'analysis_summary': {
'max_protein': filtered_df.agg({"protein_content": "max"}).collect()[0][0],
'max_oil': filtered_df.agg({"oil_content": "max"}).collect()[0][0],
'avg_disease_resistance': round(filtered_df.agg({"disease_resistance": "avg"}).collect()[0][0], 2)
}
}
return JsonResponse(result_data)
@require_http_methods(["POST"])
def environment_stress_adaptation_analysis(request):
data = json.loads(request.body)
stress_type = data.get('stress_type')
region_code = data.get('region_code')
env_df = spark.read.option("header", "true").option("inferSchema", "true").csv("/data/soybean_environment_data.csv")
stress_df = env_df.filter(col("stress_type") == stress_type).filter(col("region_code") == region_code)
adaptation_analysis = stress_df.groupBy("variety_name", "stress_level").agg(
avg("survival_rate").alias("avg_survival"),
avg("yield_reduction").alias("avg_yield_loss"),
avg("recovery_days").alias("avg_recovery_time"),
count("test_id").alias("test_count")
).filter(col("test_count") >= 5)
stress_tolerance_ranking = adaptation_analysis.withColumn("tolerance_score",
(col("avg_survival") * 0.4 - col("avg_yield_loss") * 0.35 - col("avg_recovery_time") * 0.25))
best_adapted_varieties = stress_tolerance_ranking.filter(col("stress_level") == "high").orderBy(desc("tolerance_score"))
environmental_factors = stress_df.groupBy("temperature_range", "humidity_level").agg(
avg("plant_height").alias("avg_height"),
avg("leaf_area").alias("avg_leaf_area"),
avg("root_depth").alias("avg_root_depth")
)
stress_response_patterns = stress_df.withColumn("stress_category",
when(col("stress_intensity") <= 30, "mild")
.when(col("stress_intensity") <= 60, "moderate")
.otherwise("severe")
).groupBy("variety_name", "stress_category").agg(
avg("physiological_index").alias("avg_physio_index"),
avg("biochemical_marker").alias("avg_biochem_marker")
)
adaptive_mechanisms = stress_df.select("variety_name", "osmotic_adjustment", "antioxidant_activity", "stomatal_regulation").groupBy("variety_name").agg(
avg("osmotic_adjustment").alias("avg_osmotic"),
avg("antioxidant_activity").alias("avg_antioxidant"),
avg("stomatal_regulation").alias("avg_stomatal")
)
result_data = {
'adaptation_stats': [row.asDict() for row in best_adapted_varieties.limit(15).collect()],
'environmental_factors': [row.asDict() for row in environmental_factors.collect()],
'stress_response_patterns': [row.asDict() for row in stress_response_patterns.collect()],
'adaptive_mechanisms': [row.asDict() for row in adaptive_mechanisms.collect()],
'summary_metrics': {
'total_varieties_tested': stress_df.select("variety_name").distinct().count(),
'average_survival_rate': round(stress_df.agg({"survival_rate": "avg"}).collect()[0][0], 2),
'stress_categories': stress_df.select("stress_category").distinct().count()
}
}
return JsonResponse(result_data)
@require_http_methods(["POST"])
def yield_shape_correlation_analysis(request):
data = json.loads(request.body)
analysis_scope = data.get('analysis_scope')
correlation_type = data.get('correlation_type')
yield_df = spark.read.option("header", "true").option("inferSchema", "true").csv("/data/soybean_yield_shape_data.csv")
filtered_yield_df = yield_df.filter(col("analysis_scope") == analysis_scope)
yield_shape_correlations = filtered_yield_df.select("pod_length", "pod_width", "seed_weight", "seeds_per_pod", "total_yield").groupBy("variety_id").agg(
avg("pod_length").alias("avg_pod_length"),
avg("pod_width").alias("avg_pod_width"),
avg("seed_weight").alias("avg_seed_weight"),
avg("seeds_per_pod").alias("avg_seeds_per_pod"),
sum("total_yield").alias("total_yield_sum")
)
shape_yield_vector = filtered_yield_df.select("pod_length", "pod_width", "seed_weight", "seeds_per_pod", "total_yield")
assembler_yield = VectorAssembler(inputCols=shape_yield_vector.columns, outputCol="yield_features")
yield_vector_df = assembler_yield.transform(shape_yield_vector)
yield_correlation_matrix = Correlation.corr(yield_vector_df, "yield_features").head()[0].toArray()
morphological_analysis = filtered_yield_df.withColumn("shape_index",
col("pod_length") / col("pod_width")).withColumn("seed_density",
col("seeds_per_pod") / col("pod_length")).groupBy("variety_id").agg(
avg("shape_index").alias("avg_shape_index"),
avg("seed_density").alias("avg_seed_density"),
spark_round(avg("total_yield"), 2).alias("avg_variety_yield")
)
yield_efficiency_scores = morphological_analysis.withColumn("efficiency_ratio",
col("avg_variety_yield") / (col("avg_shape_index") + col("avg_seed_density"))).orderBy(desc("efficiency_ratio"))
size_category_analysis = filtered_yield_df.withColumn("size_category",
when(col("seed_weight") <= 15, "small_seed")
.when(col("seed_weight") <= 25, "medium_seed")
.otherwise("large_seed")
).groupBy("size_category").agg(
avg("total_yield").alias("category_avg_yield"),
count("sample_id").alias("category_count"),
avg("pod_length").alias("category_avg_pod_length")
)
optimal_combinations = filtered_yield_df.filter(col("total_yield") >= filtered_yield_df.approxQuantile("total_yield", [0.8], 0.05)[0]).select("pod_length", "pod_width", "seed_weight", "seeds_per_pod").groupBy().agg(
avg("pod_length").alias("optimal_pod_length"),
avg("pod_width").alias("optimal_pod_width"),
avg("seed_weight").alias("optimal_seed_weight"),
avg("seeds_per_pod").alias("optimal_seeds_per_pod")
)
result_data = {
'yield_shape_correlations': [row.asDict() for row in yield_shape_correlations.limit(20).collect()],
'correlation_matrix': yield_correlation_matrix.tolist(),
'morphological_analysis': [row.asDict() for row in morphological_analysis.collect()],
'yield_efficiency_scores': [row.asDict() for row in yield_efficiency_scores.limit(15).collect()],
'size_category_analysis': [row.asDict() for row in size_category_analysis.collect()],
'optimal_combinations': [row.asDict() for row in optimal_combinations.collect()],
'analysis_summary': {
'total_samples': filtered_yield_df.count(),
'varieties_analyzed': filtered_yield_df.select("variety_id").distinct().count(),
'correlation_strength': abs(yield_correlation_matrix[0][4]) if len(yield_correlation_matrix) > 4 else 0
}
}
return JsonResponse(result_data)
高级大豆农业数据分析与可视化系统文档展示
💖💖作者：计算机编程小咖
💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常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。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，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！
💜💜
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
