Research Paper Reads - Fruit Freshness

Paper #1 - Deep Learning Based Classification System For Recognizing Local Spinach

• Five types of spinach. (a)Jute Spinach, (b)Malabar Spinach, (c)Red Spinach, (d)Taro Spinach, (e)Water Spinach.
• Because our dataset contains different sizes of the images. 224x224x3 is the input shape of our model

Paper #2 - Automatic Plant Cover Estimation with Convolutional Neural Networks

• This dataset contains 7,200 images with 750 training and 150 validation images per class and is therefore also balanced
• Our network consists of two main components: a feature extractor backbone and a network head
•  The backbone consists of one of the abovementioned standard classification networks potentially in conjunction with a Feature Pyramid Network to increase the output resolution

Paper #3 - Fruit Freshness Grading Using Deep Learning

• Fruit colour is derived from natural pigments when ripening, enzymatic and non-enzymatic browning reactions lead to the formation of water-soluble dark colours
• Visual characteristics, e.g., shape, wholeness, spots, bruises, and blemishes, can reflect the speed of fruit deterioration
• The consistency of physical shape may indicate the thickness of fruits that may have implications of its capability to defend against diseases
• Geometric changes are a frequently observed result of fruit degradation
• Texture is another important measurement of the level how a fruit has decayed
• Fruit texture, colour and shape are three important visual features for fruit quality grading

• Added random noises follow the sequential order: Random brightness adjustment, random contrast, and random erasion

• In total, there are (approximate) 4,000 images collected with each type of fruit about 700
• The freshness grading is scaled from 0.0 to 10.0 with 0.0 indicating total corruption and 10.0 for total freshness 
• We define the fruits being harvested as absolute freshness with a numerical level description of 10.0

Paper #4 - Machine Vision based Fruit Classification and Grading - A Review

• Some extraction methods like Speeded Up Robust Features (SURF), Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) 
• Features of fruits like color, size, shape and texture
• Automatic sorting system that can perform fast, save time and reduce manual labor
• The basic steps of the automatic image-based fruit grading are: fruit image recognition, fruit object recognition

Color features extraction methods broadly fall in two categories:

1. Global methods (global color histogram, histogram intersection, image bitmap)

2. Local methods (local color histogram, color correlogram, color difference histogram)

# Fitting K-NN to the Training set

from sklearn.neighbors import KNeighborsClassifier

classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)

classifier.fit(X_train, y_train)

# Predicting the Test set results

y_pred = classifier.predict(X_test)

Paper #5 - Deep Learning for Automatic Quality Grading of Mangoes: Methods and Insights

• Augment the CNN classifier with a convolutional autoencoder
• The rationale of considering such networks is as follows: 1) the presence of autoencoder
• forces the network to remember essential information for reconstruction when extracting features for classification, thereby having a regularizing effect; and, 2) the latent features learned by the network could benefit other downstream tasks as they contain the compressed information for reconstruction;

Data Augmentation

• Horizontal or vertical flip, each with 50% probability;
• Brightness, –20 to +20%;
• Contrast, –10 to +10%;
• Rotation, –20 to 20 degrees;
• Zoom in/out, 0.8x to 1.25x

The ConvAE-Clfs consist of 3 components:

• A convolution-based encoder that compresses an image into a latent vector;
• A convolution-based decoder that reconstructs the image from the latent vector and some intermediate features;
• A fully-connected classifier that takes the latent vector as input and gives the class prediction.

The proposed convolutional autoencoder-classifiers were shown to have no clear advantage over the single-task CNNs, but the result should be verified with larger datasets and more related tasks

Paper #6 - Deep Learning and Machine Vision for Food Processing: A Survey

• An MVS includes two main parts to enable objective and non-destructive food evaluation: 1) acquiring and 2) processing

• Fruit and Vegetable Identification Using Machine Learning for Retail Applications
• GuavaNet: A deep neuralNet: A deep neural network ar al network architecture for automatic sensory evaluation  to predict the degree of acceptability for Guava by a consumer 

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