Development of a Mobile-Based lettuce (Lactuca sativa) disease identification
| dc.contributor.author | Kristal T. Ibuyat and Ellen Michelle A. Mendoza | |
| dc.date.accessioned | 2026-02-23T07:00:24Z | |
| dc.date.available | 2026-02-23T07:00:24Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Lettuce is a type of crop that is susceptible to several diseases during cultivation and delivery to consumers. The diseases that harm lettuce plants are either bacterial or fungal in nature. Emerging technologies such as computer vision and artificial intelligence (AI) are expected to make use of huge data availability for active training, resulting in operational real-time smart devices and predictable models. Computer vision and AI driven food business refers to the phenomena of using vision and learning approaches to improve the food industry. There are several types of infections that can assault and arise through the leaf in the agriculture field. If the condition is not directed early, it might have a negative impact on the total and quality of the generation. Thus, this study was conducted to develop a system which is utilized to aid lettuce grown in detection of different diseases. The machine learning system used 5,687 total images to gain the desired accuracy for detection. Using confusion matrix, the accuracy and efficiency of the system in identifying different diseases was evaluated. After the actual test analysis, the system has 0.9663 or 96.63% accuracy in determining different lettuce diseases, while in terms of incorrect predictions, the misclassification rate is 0.0337 or 3.37%. | |
| dc.identifier.uri | http://granarium.clsu.edu.ph/handle/123456789/986 | |
| dc.language.iso | en_US | |
| dc.relation.supervisor | NICASIO C. SALVADOR, M.Sc. | |
| dc.title | Development of a Mobile-Based lettuce (Lactuca sativa) disease identification | |
| dc.type | Thesis |