AI实现葡萄叶片识别(基于深度学习的葡萄叶片识别)
基于深度学习的图像识别：葡萄叶片识别
一、项目背景与介绍
图像识别是人工智能（AI）领域的一项关键技术，其核心目标是让计算机具备像人类一样“看”和“理解”图像的能力。借助深度学习、卷积神经网络（CNN）等先进算法，图像识别技术实现了从图像信息的获取到理解的全面提升。近年来，这一技术已在医疗、交通、安防、工业生产等多个领域取得了颠覆性突破，不仅显著提升了社会生产效率，还深刻改变了人们的生活方式。葡萄叶片识别的实际应用场景
- 农业生产与种植管理
葡萄叶识别技术可以帮助农民快速、准确地识别葡萄的品种和生长状态。通过分类不同种类的葡萄叶，农民可以优化种植策略，合理分配资源（如肥料和水分），从而提高葡萄的产量和品质。此外，该技术还可以用于监测葡萄植株的生长周期，指导科学化管理。 - 病虫害检测与诊断
通过对葡萄叶的图像进行分析，葡萄叶识别技术可以检测出叶片上是否存在病害或虫害的特征。例如，可以识别霜霉病、白粉病等常见葡萄病害的早期症状，及时提醒农民采取防治措施。这种技术可以大幅减少农药的使用量，提高生态友好性。 - 食品加工与质量评估
在食品加工行业，葡萄叶是某些传统美食（如中东的葡萄叶包饭）的关键原料。葡萄叶识别技术可以用于区分不同品种的叶片，以确保其口感、大小和质量符合加工要求，从而提升加工产品的一致性和市场竞争力。 - 葡萄品种的保护与追溯
不同品种的葡萄在外观、叶片形态等方面存在差异，通过葡萄叶识别技术，可以为葡萄品种建立数字化档案。这不仅有助于保护珍稀的葡萄品种，还可以通过图像识别追溯某批次葡萄的种植来源，满足消费者对产品质量和来源的追溯需求。
二、数据预处理
# 数据增强和预处理
train_transforms = transforms.Compose([
transforms.RandomHorizontalFlip(), # 随机水平翻转
transforms.RandomRotation(10), # 随机旋转 ±10°
transforms.RandomResizedCrop(224), # 随机裁剪并调整到 224x224
transforms.ToTensor(), # 转换为张量
transforms.Normalize(mean=[0.485, 0.456, 0.406], # 归一化
std=[0.229, 0.224, 0.225])
])
test_transforms = transforms.Compose([
transforms.Resize(256), # 调整图像大小到 256
transforms.CenterCrop(224), # 中心裁剪到 224x224
transforms.ToTensor(), # 转换为张量
transforms.Normalize(mean=[0.485, 0.456, 0.406], # 归一化
std=[0.229, 0.224, 0.225])
])
三、数据读取与划分
from torch.utils.data import random_split, DataLoader
# 读取训练和测试数据
dataset = datasets.ImageFolder(root='data', transform=train_transforms)
train_size = int(0.6 * len(dataset))
val_size = int(0.2 * len(dataset))
test_size = len(dataset) - train_size - val_size
train_dataset, val_dataset, test_dataset = random_split(dataset, [train_size, val_size, test_size])
# 创建 DataLoader
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=4)
val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False, num_workers=4)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=4)
# 获取类别列表
class_names = dataset.classes
四、数据可视化
import matplotlib.pyplot as plt
import numpy as np
def imshow(tensor, title=None):
# 将张量转换为图像
image = tensor.numpy().transpose((1, 2, 0))
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
image = std * image + mean # 去归一化
image = np.clip(image, 0, 1)
plt.imshow(image)
if title is not None:
plt.title(title)
plt.show()
# 获取一个批次的数据
data_iter = iter(train_loader)
images, labels = next(data_iter)
# 可视化
for i in range(4): # 显示前4张图片
imshow(images[i], title=class_names[labels[i]])
五、模型构建
import torch.nn as nn
import torch.nn.functional as F
class LightCNN(nn.Module):
def __init__(self, num_classes):
super(LightCNN, self).__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1) # 输入: RGB 图像
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
self.fc1 = nn.Linear(32 * 56 * 56, 128) # 假设输入图像大小为 224x224
self.fc2 = nn.Linear(128, num_classes)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2(x), 2))
x = x.view(x.size(0), -1) # 展平
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
# 初始化模型
num_classes = len(class_names)
model = LightCNN(num_classes=num_classes)
六、模型训练
import torch.optim as optim
# 设备配置
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
# 损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
model.train()
train_loss = 0.0
correct = 0
total = 0
for images, labels in train_loader:
images, labels = images.to(device), labels.to(device)
# 前向传播
outputs = model(images)
loss = criterion(outputs, labels)
# 反向传播和优化
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += labels.size(0)
correct += predicted.eq(labels).sum().item()
print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {train_loss/len(train_loader):.4f}, Accuracy: {100 * correct / total:.2f}%")
# 验证模型
model.eval()
val_loss = 0.0
correct = 0
total = 0
with torch.no_grad():
for images, labels in val_loader:
images, labels = images.to(device), labels.to(device)
outputs = model(images)
loss = criterion(outputs, labels)
val_loss += loss.item()
_, predicted = outputs.max(1)
total += labels.size(0)
correct += predicted.eq(labels).sum().item()
print(f"Validation Loss: {val_loss/len(val_loader):.4f}, Accuracy: {100 * correct / total:.2f}%")
Epoch [1/10], Loss: 10.1663, Accuracy: 34.44%
Epoch [2/10], Loss: 2.1635, Accuracy: 37.22%
Epoch [3/10], Loss: 1.1604, Accuracy: 32.22%
Epoch [4/10], Loss: 1.1189, Accuracy: 41.11%
Epoch [5/10], Loss: 1.0591, Accuracy: 53.33%
Epoch [6/10], Loss: 1.0724, Accuracy: 33.89%
Epoch [7/10], Loss: 1.0264, Accuracy: 57.78%
Epoch [8/10], Loss: 0.9993, Accuracy: 55.56%
Epoch [9/10], Loss: 1.0055, Accuracy: 55.00%
Epoch [10/10], Loss: 0.9461, Accuracy: 59.44%
Validation Loss: 0.9202, Accuracy: 61.67%
可知，精度略低，模型效果欠佳，使用预训练模型优化模型性能
七、模型优化
import torch.nn as nn
from torchvision import models
class PretrainedMobileNet(nn.Module):
def __init__(self, num_classes):
super(PretrainedMobileNet, self).__init__()
# 加载预训练的 MobileNet 模型
self.model = models.mobilenet_v2(pretrained=True)
# 冻结所有参数
for param in self.model.parameters():
param.requires_grad = False
# 替换 MobileNet 的最后一层分类器
in_features = self.model.last_channel
self.model.classifier = nn.Sequential(
nn.Linear(in_features, num_classes)
)
def forward(self, x):
return self.model(x)
num_classes = len(class_names)
model = PretrainedMobileNet(num_classes=num_classes)
import torch.optim as optim
# 设备配置
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
# 损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
model.train()
train_loss = 0.0
correct = 0
total = 0
for images, labels in train_loader:
images, labels = images.to(device), labels.to(device)
# 前向传播
outputs = model(images)
loss = criterion(outputs, labels)
# 反向传播和优化
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += labels.size(0)
correct += predicted.eq(labels).sum().item()
print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {train_loss/len(train_loader):.4f}, Accuracy: {100 * correct / total:.2f}%")
Epoch [1/10], Loss: 1.0287, Accuracy: 42.78%
Epoch [2/10], Loss: 0.9017, Accuracy: 56.11%
Epoch [3/10], Loss: 0.7832, Accuracy: 73.33%
Epoch [4/10], Loss: 0.6940, Accuracy: 81.67%
Epoch [5/10], Loss: 0.6381, Accuracy: 78.89%
Epoch [6/10], Loss: 0.5677, Accuracy: 85.56%
Epoch [7/10], Loss: 0.5053, Accuracy: 87.22%
Epoch [8/10], Loss: 0.4808, Accuracy: 83.33%
Epoch [9/10], Loss: 0.4640, Accuracy: 87.22%
Epoch [10/10], Loss: 0.4638, Accuracy: 84.44%
八、模型评估
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
# 测试模型
y_true = []
y_pred = []
model.eval()
with torch.no_grad():
for images, labels in test_loader:
images, labels = images.to(device), labels.to(device)
outputs = model(images)
_, predicted = outputs.max(1)
y_true.extend(labels.cpu().numpy())
y_pred.extend(predicted.cpu().numpy())
# 计算评价指标
accuracy = accuracy_score(y_true, y_pred)
precision = precision_score(y_true, y_pred, average='macro')
recall = recall_score(y_true, y_pred, average='macro')
f1 = f1_score(y_true, y_pred, average='macro')
print(f"Test Accuracy: {accuracy:.4f}")
print(f"Precision: {precision:.4f}")
print(f"Recall: {recall:.4f}")
print(f"F1 Score: {f1:.4f}")
Test Accuracy: 0.9500
Precision: 0.9508
Recall: 0.9530
F1 Score: 0.9507
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
# 生成混淆矩阵
cm = confusion_matrix(y_true, y_pred)
cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] # 归一化
# 设置类别标签
class_labels = class_names
# 可视化混淆矩阵
plt.figure(figsize=(8, 6))
sns.heatmap(cm_normalized, annot=True, fmt=".2f", cmap="Blues", xticklabels=class_labels, yticklabels=class_labels)
plt.title("Normalized Confusion Matrix")
plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.tight_layout()
plt.show()
原文地址：https://blog.csdn.net/qq_42492056/article/details/144413391
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