A walk-behind fertilizer applicator improves precision and weed control in cotton, suggesting better farming efficiency.
A vision‐based, walk‐behind fertilizer applicator for cotton crops was designed to address the limitations of conventional systems. Traditional applicators face multiple operational issues such as high discharge rates, uneven fertilizer distribution, input wastage, and increased weed infestation. Field evaluations were carried out to measure parameters including missing plant index, uniformity of application, precision, fertilizer savings, and overall field performance. The applicator integrates three main systems: a cotton detection unit, an electronic controller, and an automatic fertilizing mechanism. The cotton detection unit captures and processes images to identify cotton plants while excluding weeds; the control system governs the stepper motor of the fertilization unit based on cotton detection signals; and the fertilizing mechanism dispenses micro‐granular fertilizer near cotton plants by activating a metering unit through the stepper motor. The detection unit showed high accuracy with bounding box loss at 2.14% and object loss at 1.56% over 100 training epochs, confirming effective real‐time detection. Small variations in discharge were observed, with Urea ranging between 5.59 and 8.9% and DAP between 6.89% and 9.25%. The left‐to‐right distribution ratios for Urea and DAP ranged from 0.89 to 1.05 and 0.90 to 1.04, respectively, with average values of 0.95 (Urea) and 0.97 (DAP). The system demonstrated high precision, delivering fertilizer with an accuracy between 90 and 93 percent, and an overall average of 91%. Theoretical and actual field capacities were in the range of 0.07–0.14 and 0.045–0.078 ha/h, respectively, while field efficiency (FE) varied from 69.39% at 0.5 km/h to 58.21% at 1 km/h. This study effectively developed a precision applicator that ensures targeted fertilization, adaptive delivery, reduced labor and time, better weed control, and promotes the use of modern technology in farming.
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Chouriya et al. (2025) studied this question.
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