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.

The development of the Internet of Things (IoT) in electronics, computer, robotics, and internet technology is inevitable and has rapidly accelerated more than before as the IoT paradigm is a promising solution in terms of solving real world problems, especially for digitizing and monitoring in real time. Various IoT schemes have successfully been applied to some areas such as smart health and smart agriculture. Since the agriculture areas are getting narrow, the development of IoT in agriculture should be prioritized to enhance crop production. This paper proposes the IoT scheme for long range communication based on Long Range (LoRa) modules applied to smart agriculture. The scheme utilizes the low power modules and long-distance communication for monitoring temperature, humidity, soil moisture, and pH soil. Our IoT design has successfully been applied to agriculture areas which have unstable network connections. The design is analyzed to obtain the maximum coverage using different spreading factors and bandwidths. We show that as the spreading factor increases to 12, the maximum coverage can be transmitted to 1000 m. However, the large coverage also comes with the disadvantages of the increased delays.

The objective of this research is to determine the quality of cocoa beans through morphology of their digital images. Samples of cocoa beans were scattered on a bright white paper under a controlled lighting condition. A compact digital camera was used to capture the images. The images were then processed to extract their morphological parameters. Classification process begins with an analysis of cocoa beans image based on morphological feature extraction. Parameters for extraction of morphological or physical feature parameters, i.e., Area, Perimeter, Major Axis Length, Minor Axis Length, Aspect Ratio, Circularity, Roundness, Ferret Diameter. The cocoa beans are classified into 4 groups, i.e.: Normal Beans, Broken Beans, Fractured Beans, and Skin Damaged Beans. The model of classification used in this paper is the Multiclass Ensemble Least-Squares Support Vector Machine (MELS-SVM), a proposed improvement model of SVM using ensemble method in which the separate hyperplanes are obtained by least square approach and the multiclass procedure uses One-Against- All method. The result of our proposed model showed that the classification with morphological feature input parameters were accurately as 99.705% for the four classes, respectively.

YOLO (You-Only-Look-Once) is a deep learning-based one-stage detection method that has been widely used and achieved great success in image classification and localization. As the state-of-the-art method, YOLO has been upgraded to version 5. This paper proposes a new approach to using a Genetic Algorithm (GA) within a YOLOv5 framework for human object detection applied in the Unmanned Aerial Vehicle (UAV) perspective image dataset. The dataset has challenges, such as a small target, the view of the object is from above, and there is an illumination and light effect. To comply with this challenge, we will utilize the dataset of visual images taken from a UAV (RGB-image) along with Thermal Infrared (TIR) information. GA is used for optimizing the Hyperparameter, which is one of the critical factors in determining the model’s performance. Based on our numerical experiments, we found that this YOLOv5-based transfer learning method using RGB-TIR dataset and optimized by GA can achieve higher accuracy compared with the original YOLOv5 for Human Detection on Unmanned Aerial Vehicle Perspective. The objective of this research is to create a surveillance system that will be used to monitor a wide area using autonomous UAVs that can exchange information with each other. In the end, the solution from this research can help related parties in tackling the problem of illegal activities with limited human resources.

Smart Farming (SF) has garnered interest from computer science researchers for its potential to address challenges in Smart Farming and Precision Agriculture (PA). This systematic review explores the application of Fuzzy Logic (FL) in these areas. Using a specific anonymous search method across five scientific web indexing databases, we identified relevant scholarly articles published from 2017 to 2024, assessed through the PRISMA methodology. Out of 830 selected papers, the review revealed four gaps in using FL to manage imprecise data in Smart Farming. This review provides valuable insights into FL for potential applications and areas needing further investigation in SF.

Farming is a fundamental factor driving economic development in most regions of the world. As in agricultural activity, labor has always been hazardous and can result in injury or even death. This perception encourages farmers to use proper tools, receive training, and work in a safe environment. With the wearable device as an Internet of Things (IoT) subsystem, the device can read sensor data as well as compute and send information. We investigated the validation and simulation dataset to determine whether accidents occurred with farmers by applying the Hierarchical Temporal Memory (HTM) classifier with each dataset input from the quaternion feature that represents 3D rotation. The performance metrics analysis showed a significant 88.00% accuracy, precision of 0.99, recall of 0.04, F_Score of 0.09, average Mean Square Error (MSE) of 5.10, Mean Absolute Error (MAE) of 0.19, and a Root Mean Squared Error (RMSE) of 1.51 for the validation dataset, 54.00% accuracy, precision of 0.97, recall of 0.50, F_Score of 0.66, MSE = 0.06, MAE = 3.24, and = 1.51 for the Farming-Pack motion capture (mocap) dataset. The computational framework with wearable device technology connected to ubiquitous systems, as well as statistical results, demonstrate that our proposed method is feasible and effective in solving the problem’s constraints in a time series dataset that is acceptable and usable in a real rural farming environment for optimal solutions.