Predicting the Planting Time of Bird's Eye Chili Based on Environmental Conditions Using Internet of Things (IoT) and Neural Network Method

Abstract

In Indonesian cuisine, Red Tabasco pepper holds a significant place as a commonly used ingredient. However, the cultivation of this chili variety is not without its challenges, primarily due to the volatile nature of chili prices. Farmers often grapple with the critical decision of when to plant Tabasco pepper to optimize their yields and income. Understanding the complexities of this decision-making process in the context of varying environmental conditions is crucial. Thanks to recent advancements in Internet of Things (IoT) technology, innovative systems have emerged to address these challenges.This study delves into the development of an IoT-based solution aimed at assisting farmers in precisely determining the optimal planting time for Tabasco pepper. It leverages five key criteria—average temperature (°C), average humidity (%), rainfall (mm), length of sunlight (hours), and groundwater usage data (m3)—to make data-driven planting decisions. The pressing need for such a system becomes evident when considering the unpredictability of climate patterns and their direct impact on crop outcomes. Utilizing historical data from 2019, obtained from the DKI Jakarta Provincial Government Open Data, and climate data from the Meteorological Agency, Climatology, and Geophysics (BMKG), the authors have successfully developed an IoT-based prototype. This prototype employs a neural network algorithm to analyze the aforementioned criteria. The outcome is a reliable prediction system that boasts an impressive accuracy rate of 91.26%. By offering this level of precision in determining the ideal planting time for Tabasco pepper, the system extends invaluable support to farmers, helping them optimize their cultivation practices and navigate the uncertainties of the chili market.