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Machine Learning Algorithms and Applications


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ML algorithms (KNN, decision trees, and SVM). ML algorithms provide accuracy over 90% but fail when the input data is of a different class (breed in terms of sericulture field) when compared to the class for which the algorithm was trained since the color of eggs is not the same for different breeds of the silkworm. To overcome this issue, a supervised CNN technique is used which requires the true label while the features are selected automatically. The primary aim of our approach was to accurately count several silkworm eggs present in a given digital image and further classify them into respective classes such as hatched and unhatched. Figure 2.2 represents a sample digital image of the egg sheet with a different class of eggs being marked with specific colors manually. The eggs marked with green color represent the hatched class (HC), while eggs marked with red color represent the unhatched class (UHC).

      To identify the core features of the eggs, for segmenting them from the background sheet and to classify them into respective categories, a simple deep learning technique was used with four hidden layers to provide results that are much more accurate compared to conventional methods. Deep learning models are trained using TensorFlow framework to provide three different results such as foreground-background segmentation, detecting eggs, and classifying detected egg.

Schematic illustration of silkworm egg classes: hatched eggs and unhatched eggs. Schematic illustration of core CNN model.

      2.3.2 Foreground-Background Segmentation

      The basic requirement for accurate counting of silkworm egg is to perform foreground-background (FB) segmentation. In the previous attempts, the background was segmented based on the intensity value of the eggs [8]. The region that has no pixel values corresponding to the eggs is considered as background and discarded before the image processing stage. However, this is not ideal in all situations, since the silkworm eggs laid on the sheet may also contain urine from the silkworms that discolor the background. The urinated background dries into a white layer that resembles an egg pixel intensity value close to 230 for an 8-bit grayscale image.

      The resolution of the digital data that is fed into the CNN model is another reason to perform segment the background before the class of eggs is determined. The eggs are of the size around 32 × 32 pixels after scanning, while the entire sheet of silkworm eggs is of the size 5008 × 6984 pixels (in our experiment). If the entire sheet was fed to the CNN model with an input image size of 32 × 32 pixels with a sliding window method, then the model must classify 35M images that would be computationally expensive for a system without GPU support.

Input image Activation/output Training samples Test samples Validation samples Test loss Accuracy on the test set Accuracy on the validation set
128 × 128 SoftMax 2 class-(0/1) 142 × 103 64.3 × 103 9.6 × 103 0.1242 96.1554% 96.6422%
Schematic illustration of foreground-background segmentation of entire silkworm egg sheet (a) (input) and (b) (output).

      2.3.3 Egg Location Predictor