Journal of Integrated Artificial Intelligence Science and Engineering

Using computer vision to detect, classify, and map coffee fruits during harvest

2025-02-08T14:35:43+07:00

Computer vision is a technology that integrates image processing and pattern recognition to extract information from digital images. This research uses the you only look once (YOLO) method to detect, classify, and map the maturity stage of coffee fruit during harvest. The YOLOv3-tiny model was applied to classify coffee fruits into three categories including unripe, ripe, and overripe. Results showed an average precision of 86.0%, 85.2%, and 80.0%, respectively. The system enables more efficient harvest management by utilizing spatial and temporal information for coffee fruit quality mapping. This mapping can help farmers reduce operational costs and improve efficiency through the application of precision farming techniques. This technology proves great potential in improving the quality and quantity of coffee yields with a fast and accurate computer-based approach.

A combination of computer vision and laser-light backscattering imaging, oil palm fruit maturity is classified

2025-02-08T14:41:50+07:00

Laser-light backscattering imaging (LLBI) is an optical imaging technique that records the interaction of light with plant tissue, generating light reflection data relevant for assessing product quality. This research combines LLBI and an RGB-based computer vision system to non-destructively classify the maturity level of oil palm fresh fruit bunches (FFBs). This technique offers a faster, more cost-effective and more accurate solution than traditional methods. The analysis includes RGB imaging and light reflection, where parameters such as color intensity, principal axis length, and area are analyzed using image processing algorithms. Results showed that the combination of LLBI and a computer vision system significantly improved the accuracy of ripeness classification, with a strong correlation between imaging parameters and quality attributes such as oil content and color. This approach provides an important step in improving harvesting efficiency and the production of high-quality palm oil.

Thermal imaging as an indicator of oil palm fresh fruit bunches

2025-02-08T14:55:51+07:00

Classification of oil palm fresh fruit bunches utilising multiband optical sensors

2025-02-08T14:58:02+07:00

Indonesia, as the world's largest palm oil producer, faces the challenge of improving crude palm oil (CPO) production efficiency through the classification of fresh fruit bunch (FFB) maturity levels. This study aims to develop a FFB classification system using multiband optical sensors based on visible light and near-infrared spectra. A total of 191 FFB samples were classified into two categories, namely ripe and unripe, using the reflectance of the NIR spectrum. Results show that the combination of visible and NIR spectra at a wavelength of 660 nm has high accuracy in detecting oil content in FFB. The classification model based on oil content showed an accuracy of 66.7%, better than the visual inspection model (52.1%). This study shows the great potential of optical sensor technology to improve the efficiency and quality of the palm oil industry in Indonesia.

Utilising machine learning and image processing, this computer vision system detects defects and improves tomato quality

2025-02-08T15:01:50+07:00

Computer vision is a technology that integrates image processing and machine learning to evaluate product quality objectively and non-destructively. This study establishes a computer vision system for defect detection and quality assessment of tomatoes by image processing methods and machine learning algorithms, including support vector machine (SVM) and artificial neural network (ANN). The procedure commences with the collection of tomato photos, succeeded by pre-processing, segmentation, and the extraction of features related to colour, texture, and shape. Machine learning models are subsequently utilised to categorise tomatoes according to their ripeness levels and the existence of faults. The results indicate that the system can accurately detect flaws and evaluate the quality of tomatoes, demonstrating superior efficiency compared to manual techniques. This method is anticipated to enhance the uniformity of quality standards and diminish waste in the agriculture sector.