Feature Extraction for Cocoa Bean Digital Image Classification Prediction for Smart Farming Application

Yudhi Adhitya,Jenq-Shiou Leu,Setya Widyawan Prakosa,Mario Köppen

doi:10.3390/agronomy10111642

Abstract

The implementation of Industry 4.0 emphasizes the capability and competitiveness in agriculture application, which is the essential framework of a country’s economy that procures raw materials and resources. Human workers currently employ the traditional assessment method and classification of cocoa beans, which requires a significant amount of time. Advanced agricultural development and procedural operations differ significantly from those of several decades earlier, principally because of technological developments, including sensors, devices, appliances, and information technology. Artificial intelligence, as one of the foremost techniques that revitalized the implementation of Industry 4.0, has extraordinary potential and prospective applications. This study demonstrated a methodology for textural feature analysis on digital images of cocoa beans. The co-occurrence matrix features of the gray level co-occurrence matrix (GLCM) were compared with the convolutional neural network (CNN) method for the feature extraction method. In addition, we applied several classifiers for conclusive assessment and classification to obtain an accuracy performance analysis. Our results showed that using the GLCM texture feature extraction can contribute more reliable results than using CNN feature extraction from the final classification. Our method was implemented through on-site preprocessing within a low-performance computational device. It also helped to foster the use of modern Internet of Things (IoT) technologies among farmers and to increase the security of the food supply chain as a whole.

Highlights

Information technology has shifted very significantly in human life
The digital images of the cocoa beans for the study were procured from South Sulawesi, Indonesia
The sampling method was based on [35,36]. These cocoa bean samples were classified as the following: (1) whole beans, cocoa beans with a whole seed coat covering all of the seed parts and not showing any fracture (Figure 2a); (2) broken beans, a cocoa bean with a missing portion that is half (1/2) or less than the whole cocoa bean (Figure 2b); (3) beans fractions, a cocoa bean fraction that is less than half

Summary

Introduction

Information technology has shifted very significantly in human life. It is undeniable that technology currently represents an essential role in the development process from time to time.We are entering the Industrial Revolution Era 4.0, where Internet of Things (IoT) technologies are very influential in everyday life. Information technology has shifted very significantly in human life. It is undeniable that technology currently represents an essential role in the development process from time to time. We are entering the Industrial Revolution Era 4.0, where Internet of Things (IoT) technologies are very influential in everyday life. Even in the area of agriculture, such technologies [1,2] have many important roles. Feature extraction is an artificial intelligence (AI) method that selects or consolidates numerous variables as a feature, which can effectively decrease the substance of data processed while still representing the fundamental dataset. The primary feature extraction for texture analysis, developed around the 1970s, employs co-occurrence matrix features introduced by [3]

Methods

Results

Discussion

Conclusion