Predictive Modeling of Technology Adoption in Small-Scale Agriculture using a Multi-Factor Approach

Agricultural adoption is essential for hygiene food, healthy fruit, and organic vegetables, but according to applicable technology, incompatibility persists due to diverse issues. This research utilizes predictive modeling, consisting of random forest and logistic regression, to apply advanced technology with small-scale agriculture in farming districts. Significant forecasts are composed of agricultural training and education to take advantage of innovative technology to perceive the maximum outcomes and minimize cost. Moreover, web-based applications are integrated for data collection and business analytics to perceive and endorse cost-effectiveness and efficient productivity in various aspect including user information by verifying One-Time Password (OTP) to log in each time to engage in personal data protection policy, vaccination schedule, reminders, advertisement, customers and suppliers list, currency to calculate for business plan, and real time monitoring from CCTV. These experiments provide farmer insights into agricultural development and improvement for both short- and long-term contribution, especially varied seasonal situation.