Loyd S Echalar and Arnel C. Fajardo, Technological Institute of the Philippine, Philippines

The Philippines is known to be a country that values the agricultural sector. Agriculture is the backbone of the Philippine economy, contributing around 9% to its gross domestic product (GDP) and providing livelihood to millions of Filipinos. Local vegetables such as pechay, mustasa, sitaw, talong, and ampalaya are some of these essential agricultural crops, used in different famous dishes in the country. The emergence of technology helps individual and community improve their way of administering and managing crops, which is why it is very important to develop an innovative way to produce sustainable vegetable crops. The focus of this paper is on the creation of an application that can effectively categorize the ailments, pests, and nutrient deficiencies found in vegetable crops. This application uses different Convolutional Neural Networks architectures such as ResNet, YOLO, and Faster R-CNN to dissect information from digital photographs. By offering diverse insights into diseases, pests, and deficiencies, this application equips users with the knowledge to effectively handle and nurture crops, ensuring sustainable production. The mobile application helps many vegetable growers identify the problems and challenges of their crops. The used CNN architecture provides accurate detection, analysis, and interpretation of the content of digital photographs, and served as a way to provide information on the solutions. ResNet architecture provides a high accuracy rate among YOLO and Faster R-CNN in the detection and classification of diagnosis.

Local Agricultural Vegetables, Diagnosis, Deep Neural Networks, Classification ResNet, YOLO, Faster R-CNN