Tomato Plant Growth Monitoring System Using Computer Vision and Deep Learning - Singgih Wisnu Pranata, Sigit Widiyanto, Hustinawati (Mathematical Statistician and Engineering Applications, 2023)
The utilization of information technology or agricultural systems aims to support agricultural efficiency and productivity. Manual monitoring, from planting seeds to harvest time on a plant, has many limitations caused by human physical factors, including fatigue, discontinuity, non-uniformity, and inaccuracy in making observations. The monitoring system is a process to collect data from various sources. This study conducted monitoring by observing the growth of tomato plants in AIDRO's Green House. AIDRO's Green House lacked farmers and could not constantly or continuously observe plants. These conditions can affect the quality of crops and yields. The process of monitoring tomato growth in this study was carried out using digital image processing and a microcontroller to observe and analyze an object without having direct contact with the object being observed. This research also develops a forecasting system for future plant growth. The results of this study showed that the accuracy of the prediction model for the area of tomato plants from the age of 8 days to 19 days of tomato plants had an average of 92%. The accuracy obtained proves that the model is accurate in detecting tomato plants. The results of this study showed that the accuracy of the prediction model for the area of tomato plants from the age of 8 days to 19 days of tomato plants had an average of 92%. The accuracy obtained proves that the model is accurate in detecting tomato plants. The results of this study showed that the accuracy of the prediction model for the area of tomato plants from the age of 8 days to 19 days of tomato plants had an average of 92%. The accuracy obtained proves that the model is accurate in detecting tomato plants.