使用Python实现深度学习模型：智能植物生长监测与优化

import pandas as pd

# 加载植物生长数据
data = pd.read_csv('plant_growth_data.csv')

# 查看数据结构
print(data.head())

from sklearn.preprocessing import StandardScaler

# 处理缺失值
data = data.dropna()

# 提取特征和标签
X = data.drop('growth_index', axis=1)  # 特征列
y = data['growth_index']  # 标签列

# 数据标准化
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# 构建模型
model = Sequential()
model.add(Dense(64, input_dim=X.shape[1], activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1, activation='linear'))

# 编译模型
model.compile(optimizer='adam', loss='mean_squared_error', metrics=['mae'])

from sklearn.model_selection import train_test_split

# 划分数据集
X_train, X_val, y_train, y_val = train_test_split(X_scaled, y, test_size=0.2, random_state=42)

# 训练模型
history = model.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_val, y_val))

# 保存模型
model.save('plant_growth_model.h5')

# 加载模型
model = tf.keras.models.load_model('plant_growth_model.h5')

# 评估模型
loss, mae = model.evaluate(X_val, y_val)
print(f'验证损失: {loss:.4f}, 平均绝对误差: {mae:.4f}')

import numpy as np

# 输入新样本进行预测
new_data = [[value1, value2, value3, ...]]  # 新的环境和土壤数据
new_data_scaled = scaler.transform(new_data)
prediction = model.predict(new_data_scaled)
print(f'预测的植物生长指标: {prediction[0][0]:.2f}')

import matplotlib.pyplot as plt

# 绘制训练过程中的损失曲线
plt.plot(history.history['loss'], label='train_loss')
plt.plot(history.history['val_loss'], label='val_loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()