Development and evaluation of a machine vision-based cotton fertilizer applicator

Abstract

Aim of study: To develop and assess a cotton fertilizer applicator integrated with a Machine Vision Based Embedded System (MVES) to achieve precise and site-specific fertilization. Area of study: The investigation was performed in the Indian Institute of Technology, Kharagpur. Material and methods: The MVES included a cotton detection system with a web camera, processor (computer), and python-based algorithm, and a fertilizer metering control unit with a stepper motor, motor driver, power supply, and microcontroller. The python-based algorithm in the computer predicts the presence (or absence) of cotton plants, whenever an input image is received from the camera. Upon cotton detection, it transforms into a Boolean signal sent to the micro-controller via PySerial communication, which instructs the motor to rotate the metering unit. Motor adjusts the speed of metering unit based on machine speed measured through a hall sensor, ensuring site-specific delivery of metered fertilizer A developed lab setup tested the MVES, experimentally examining performance indicators. Main results: The MVES obtained a MAPE of 5.71% & 8.5%, MAD 0.74 g/plant & 1.12 g/plant for urea and DAP (di-ammonium phosphate), respectively. ANOVA revealed no statistically significant effect of forward speed on the discharge fertilizer amount (p>;0.05). For urea, discharge rates ranged from 1.03 g/s (at 10 rpm, 25% exposure length of metering unit) to 40.65 g/s (at 100 rpm, 100% exposure). DAP ranged from 1.43 to 47.66 g/s under similar conditions. Research highlights: The delivered application dosage conformed the recommended dosage. The developed MVES was reliable, had a quick response, and worked properly.