Spring Boot integrates Java Deeplearning4j to realize agricultural product quality testing system
1. Introduction
Agricultural product quality testing is crucial, it is directly related to the health of consumers and the sustainable development of the agricultural industry. However, traditional agricultural product quality testing methods have many limitations. Although artificial sensory detection is simple and fast, it has high experience and technical requirements for the inspectors, and is subjective and difficult to guarantee accuracy. Chemical analysis methods are complex, time-consuming and laborious, and require professional laboratory equipment and technical personnel. Although instrument analysis methods can provide more accurate quality information, the equipment is expensive and has high maintenance costs, and are not suitable for large-scale agricultural product testing.
With the continuous development of technology, image recognition technology plays an increasingly important role in agricultural product quality inspection. This article will describe how to use itSpring BootIntegrationJava Deeplearning4jBuild a quality inspection system for agricultural products to detect fruitsMaturityanddefect, improve the quality of agricultural products.
2. Technical Overview
- Spring Boot:Spring Boot is a framework for quickly building standalone, production-level Spring applications. It simplifies the development of Spring applications, provides features such as automatic configuration, start-up dependencies and built-in servers, allowing developers to focus more on the implementation of business logic.
- Java Deeplearning4j:Deeplearning4j is a Java-based deep learning library that supports a variety of deep learning algorithms and neural network architectures. It provides efficient numerical calculation and parallel processing capabilities, suitable for training and inference of large-scale data.
- Image recognition technology: Image recognition refers to the technology in which computers detect, classify and identify targets in images. In agricultural product quality testing, image recognition technology can judge the ripening degree and defects of the fruit by analyzing the appearance characteristics of the fruit, such as color, shape, size and texture.
3. Neural network selection
In this case, we chose to use a convolutional neural network (Convolutional Neural Network，CNN) to achieve fruit maturity and defect detection. The reason for choosing CNN is that it performs well in image recognition tasks.Features can be extracted from images efficiently through the structure of the convolutional layer and the pooling layer,likeedge、Textureandshapeetc. It is very suitable for processing agricultural product images.
There are several important reasons for choosing a convolutional neural network:
- Strong image feature extraction capability: CNN can automatically extract rich features such as edges, textures, shapes, etc. from images. For agricultural product quality testing, these characteristics are crucial to judging the maturity and defects of fruits. For example, ripe fruits are often brighter in color and have clearer textures, while defective fruits may have broken edges or abnormal color distribution. CNN can effectively extract these features through a combination of convolutional layer and pooling layer, thereby improving the accuracy of detection.
- Applicability to fruit quality testing: The quality detection of fruits mainly depends on the analysis of the appearance characteristics of fruits. CNN is very suitable for processing image data, and can accurately identify the shape, color, size and other characteristics of the fruit, thereby determining the ripening degree of the fruit and whether there are defects. Furthermore, CNN has some invariance in translation, rotation, and scaling of images, which means that CNN can still accurately detect even if the position, angle, or size of the fruit changes.
- A large number of success stories and research support: In the field of image recognition, CNN has achieved great success. Many studies and practical applications have proved the effectiveness of CNN in tasks such as object recognition, object detection, and image segmentation. In terms of fruit quality detection, many studies have also achieved good results using CNN. For example, some studies used CNN to classify fruit ripening with an accuracy of more than 90%. These success stories and research support provide a strong basis for our choice of CNN.
- Scalability and flexibility:CNN can be expanded and adjusted according to different needs. We can improve the complexity and performance of the model by increasing the number of convolutional and fully connected layers, and we can also adapt to different image features by adjusting the size and number of convolutional kernels. In addition, CNN can also be combined with other deep learning technologies, such as recurrent neural networks (RNNs), generative adversarial networks (GANs), etc., to further improve the accuracy and reliability of fruit quality detection.
IV. Dataset format
4.1 Dataset Source
We can download the published fruit image dataset from the Internet, or collect fruit image data by ourselves. When collecting data, diversity and representation of the data should be ensured, including images of fruits of different species, different maturity and different defects.
4.2 Dataset format
We will collect datasets containing images of normal and abnormal agricultural products. Each image in the dataset will be associated with relevant tag information, such as the ripening degree of the fruit, whether there are defects, etc. The directory structure can be classified and stored, such as classified and stored according to fruit type, maturity level, etc.
Datasets are usually stored in the form of image files, each image file corresponds to a fruit sample.. Image files can be in common image formats, such asJPEG、PNGwait. To facilitate management and use of datasets, we can classify and store image files by different categories and labels. For example, ripe fruit images can be stored in one folder and defective fruit images in another folder.
Images in the datasetHigh resolution format, usually224x224pixels or higher to ensure that the detailed features of the fruit can be captured clearly. Color mode adoptsRGBMode can truly reflect the color information of the fruit. Such a setting helps the convolutional neural network to better extract image features and improve the accuracy of fruit quality detection.
4.3 Sample Table
Here is a simple dataset sample table showing the file name, category, and label of the fruit image:
| file name | category | Label |
|---|---|---|
| apple | Mature | |
| apple | Flawed | |
| banana | Mature | |
| banana | Immature |
4.4 Directory Structure
Here is a possible dataset directory structure:
dataset/ ├── apples/ │ ├── mature/ │ │ ├── │ │ ├── │ │ └──... │ └── defective/ │ ├── │ ├── │ └──... ├── bananas/ │ ├── mature/ │ │ ├── │ │ ├── │ │ └──... │ └── unripe/ │ ├── │ ├── │ └──... └──...
5. Technology realization
5.1 Maven dependencies
When integrating Java Deeplearning4j using Spring Boot, you need to add the following Maven dependencies to the project's file:
<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>deeplearning4j-core</artifactId>
<version>1.0.0-M1.4</version>
</dependency>
<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>deeplearning4j-nn</artifactId>
<version>1.0.0-M1.4</version>
</dependency>
<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>deeplearning4j-ui</artifactId>
<version>1.0.0-M1.4</version>
</dependency>
<dependency>
<groupId></groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>5.2 Data loading and preprocessing
First, we need to load the dataset and preprocess it. You can use Deeplearning4jDataSetIteratorThe interface is used to load the image dataset and perform preprocessing operations such as normalization and data enhancement on the image. Here is a sample code:
import ;
import org.;
import org.;
import org.;
import ;
import ;
import ;
public class DataLoader {
public static ListDataSetIterator loadData(String dataDirectory) {
NativeImageLoader imageLoader = new NativeImageLoader(28, 28, 3);
List<File> imageFiles = new ArrayList<>();
//Transf the dataset directory and collect image files File[] directories = new File(dataDirectory).listFiles(File::isDirectory);
for (File directory : directories) {
File[] files = ();
for (File file : files) {
(file);
}
}
// Create image dataset float[][] images = new float[()][28 * 28 * 3];
int[] labels = new int[()];
for (int i = 0; i < (); i++) {
File imageFile = (i);
String label = ().getName();
labels[i] = (label);
float[] image = (imageFile);
images[i] = image;
}
// Data normalization DataNormalization scaler = new ImagePreProcessingScaler(0, 1);
(images);
(images);
// Create a dataset iterator return new ListDataSetIterator(images, labels, 10);
}
}5.3 Model construction and training
Next, we need to build a convolutional neural network model and train it. You can use Deeplearning4jMultiLayerConfigurationandMultiLayerNetworkClasses to build and train models. Here is a sample code:
import org.;
import org.;
import org.;
import org.;
import org.;
import org.;
import org.;
import org.;
import org.;
public class FruitQualityDetector {
public static MultiLayerNetwork buildModel() {
MultiLayerConfiguration configuration = new ()
.seed(123)
.weightInit()
.updater(org..XAVIER_UNIFORM)
.list()
.layer(0, new (5, 5)
.nIn(3)
.stride(1, 1)
.nOut(16)
.activation()
.build())
.layer(1, new (3, 3)
.stride(1, 1)
.nOut(32)
.activation()
.build())
.layer(2, new ()
.nOut(512)
.activation()
.build())
.layer(3, new ()
.nOut(2)
.activation()
.build())
.build();
return new MultiLayerNetwork(configuration);
}
public static void trainModel(MultiLayerNetwork model, ListDataSetIterator iterator, int epochs) {
();
for (int i = 0; i < epochs; i++) {
(iterator);
();
}
}
}5.4 Model prediction and evaluation
Finally, we can use the trained model to predict the new fruit image and evaluate the performance of the model. Here is a sample code:
import org.;
import org.;
import org.;
import org.;
import org.;
import ;
import ;
public class ModelEvaluator {
public static void evaluateModel(MultiLayerNetwork model, String testDataDirectory) {
NativeImageLoader imageLoader = new NativeImageLoader(28, 28, 3);
File[] testFiles = new File(testDataDirectory).listFiles();
float[][] testImages = new float[][28 * 28 * 3];
int[] testLabels = new int[];
for (int i = 0; i < ; i++) {
File testFile = testFiles[i];
String label = ().getName();
testLabels[i] = (label);
float[] image = (testFile);
testImages[i] = image;
}
DataNormalization scaler = new ImagePreProcessingScaler(0, 1);
(testImages);
(testImages);
Evaluation evaluation = (new ListDataSetIterator(testImages, testLabels, 10));
(());
}
}VI. Unit Test
To ensure the correctness and reliability of the code, we can write unit tests to test functions such as data loading, model building and training, model prediction and evaluation. Here is an example unit test code:
import org.;
import org.;
import ;
import ;
import static ;
public class FruitQualityDetectorTest {
private String dataDirectory;
private MultiLayerNetwork model;
private ListDataSetIterator iterator;
@BeforeEach
public void setUp() {
dataDirectory = "path/to/dataset";
iterator = (dataDirectory);
model = ();
}
@Test
public void testBuildModel() {
assertEquals(4, ().length);
}
@Test
public void testTrainModel() {
(model, iterator, 10);
assertEquals(true, ().getLayers()[0].getParam("W").isInitialized());
}
@Test
public void testEvaluateModel() {
String testDataDirectory = "path/to/test/dataset";
(model, testDataDirectory);
// Assertions can be made based on specific evaluation indicators }
}7. Expected output
- Data loading and preprocessing: Successfully load the data set and perform preprocessing operations such as normalization and data augmentation, and output information such as the size and label distribution of the data set.
- Model building and training: Successfully constructed and trained the convolutional neural network model, and output information such as loss function value and accuracy during the training process.
- Model prediction and evaluation: Successfully predict new fruit images using the trained model and output the model's evaluation indicators, such as accuracy, recall and F1 value.
8. References
Deeplearning4j official documentation
Spring Boot Official Documentation
Introduction to convolutional neural network
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