Year: 2020
Type: article
Abstract: Agricultural inputs are very important; however, these are not always available to farmers.This study aimed to develop a mobile-based eggplant diseases recognition system using image processing techniques to provide information to the farmers with regard to the identification of eggplant diseases.Quantitative method using descriptive survey was used to gather data.Weighted mean was also used to analyze the rating on the extent of compliance of the system along with ISO/IEC 25010 Software Quality Standards.Convenience sampling was employed in selecting the participants.The study employed Scrum Methodology which takes an iterative approach to software development.It also utilized different image processing techniques and adapted the MobilenetV2 framework.The study found out that farmers encountered issues such as lack of modern agricultural inputs, capital, labor force, price fluctuations, production costs, water system, and unsupervised use of chemicals.They also encountered challenges such as the presence of insect pests and diseases, natural disaster, drought, and limited access to production inputs and equipment.Result of the system evaluation using ISO/IEC 25010 Software Quality Standards shows that the system is compliant to a great extent with overall weighted mean of 4.12.Based on the findings of the study, the developed mobile application can be of great help to the farmers in identifying the different eggplant pests and diseases as well as in providing the necessary management and control.
Author: Jake Guabes Maggay
Institutions: Cagayan State University, Cagayan State University-Lasam
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W3009163848