Iberoamerican Journal of Applied Computing

INTEGRATION OF RFID TECHNOLOGY IN AGRICULTURAL TRACEABILITY TO OPTIMIZE CONTROL AND QUALITY OF PRODUCTS

2024-08-12T13:49:28+00:00

Traceability in the agricultural chain is essential to guarantee the safety and quality of food products. The use of technologies such as Radio Frequency Identification (RFID) presents itself as a solution to improve this process, providing accurate and efficient tracking of products throughout the entire production chain. This study aimed to develop and validate an integrated structure using RFID in the traceability process in the soybean grain production chain. The results of this work included the detailed specification of the stages of the production process, using RFID for tracking and monitoring. The integration of RFID proved to be effective, allowing more accurate and efficient traceability, ensuring compliance with quality standards and industry regulations.

PLANT SPECIES RECOGNITION USING LEAF IMAGES AND CONVOLUTIONAL NEURAL NETWORKS

2024-05-20T13:51:50+00:00

The identification of plant species is essential in botany and has attracted the interest of researchers in the field of computer science. Such identification requires the assistance of botany experts and, due to the substantial number of species and the similarity among them, it can be time-consuming and subjective. To automate the process of plant identification, computer systems that capture and process plant images have been considered. These systems use machine learning and therefore require image samples for training and model construction. Among the techniques that can be used for machine learning, convolutional neural networks have shown promise due to their ability to use images without prior preprocessing and background information. This work investigates the use of machine learning through convolutional neural networks to identifying plant species. For this, a new dataset of images from 35 plant species were created, collecting images from an arboreal collection, and, using data augmentation, this dataset was expanded. This dataset was used to evaluate the accuracies of four convolutional neural network models. The better accuracy value was equal to 89%, when using the MobileNetV2 model.

ASPHALT PAVEMENT CRACKING CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS

2024-06-20T19:15:05+00:00

Asphalt pavements on roads are subject to cracking due mainly to overload and weather over time. Detecting the presence of cracking is an essential part of road maintenance systems. Traditional road defect detection methods are time-consuming, dangerous, labor-intensive, and subjective. An alternative is to use digital images of roads, collected by cars or unmanned aerial vehicles, to feed automated asphalt damage identification models. Thus, automated defect detection systems could quantify the quality of road surfaces and help prioritize and plan road network maintenance, thereby preserving and extending the useful life of roads. This work aims to investigate the use of machine learning for detecting and classifying cracking in road pavements using convolutional neural networks. Transfer learning and data augmentation techniques were used to build classification models. The results indicated that the model was able to identify cracking in the images of roads with an accuracy of 99%. When using the model to classify the cracking subtypes, the model presented an overall accuracy of 78%.