写一个有关田间杂草识别系统的源程序

创建一个田间杂草识别系统的基本源程序涉及到计算机视觉、深度学习和机器学习技术，这里我会给出一个简单的Python示例，使用TensorFlow库实现基于卷积神经网络(CNN)的分类模型。假设我们已经有了训练好的数据集，包含了田间的作物图片和对应的杂草图片。

# 导入必要的库
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

# 数据预处理
train_datagen = ImageDataGenerator(rescale=1./255)
val_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    'train_data', # 训练数据目录
    target_size=(150, 150), # 图片尺寸
    batch_size=32,
    class_mode='categorical')

validation_generator = val_datagen.flow_from_directory(
    'val_data', # 验证数据目录
    target_size=(150, 150),
    batch_size=32,
    class_mode='categorical')

# 构建CNN模型
model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Conv2D(128, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Flatten(),
    Dense(512, activation='relu'),
    Dense(train_generator.num_classes, activation='softmax')])

# 编译模型
model.compile(optimizer='adam',
              loss=tf.keras.losses.categorical_crossentropy,
              metrics=['accuracy'])

# 训练模型
history = model.fit(
    train_generator,
    steps_per_epoch=train_generator.samples // train_generator.batch_size,
    epochs=20,
    validation_data=validation_generator,
    validation_steps=validation_generator.samples // validation_generator.batch_size)

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