TY  - JOUR
T1  - AI-Powered Subterranean Crop Harvesting Stage Detection and Automation
AU  - M D, Rakesh
AU  - S B, Rudraswamy
PY  - 2025
DA  - December
Y2  - 2025
DO  - 10.35378/gujs.1562408
JF  - Gazi University Journal of Science
PB  - Gazi University
WT  - DergiPark
SN  - 2147-1762
SP  - 1819
EP  - 1833
VL  - 38
IS  - 4
LA  - en
AB  - This work introduces a system that combines deep learning with robotics to automate the detection and harvesting of beetroot crops. The system utilizes a convolutional neural network (CNN) based on the ResNet-50 architecture for image classification and is trained to identify beetroot plants at their ideal harvesting stage. With an accuracy of 99.08% and a precision of 98.39%, the model ensures dependable detection. A robotic platform, equipped with a camera, captures images in the field, which are processed by the ResNet-50 model to assess the readiness of the beetroots. Once a beetroot is confirmed ready for harvest, a robotic arm is triggered to carry out the harvesting operation. This system tackles the difficulty of timely and accurate crop identification, automating a critical aspect of the harvesting process. By leveraging deep learning for detection and robotics for execution, the system aims to minimize manual oversight and improve the effectiveness of beetroot harvesting operations.
KW  - Agriculture Automation
KW  - Deep Learning
KW  - CNN
KW  - Harvesting
KW  - Robotics
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UR  - https://doi.org/10.35378/gujs.1562408
L1  - https://dergipark.org.tr/en/download/article-file/4267882
ER  -