YOLOv4 and Tiny YOLOv4 Based Forage Crop Detection with an Artificial Intelligence Board

Abstract

The decrease in the possibilities of increasing the arable agricultural areas in the world and the continuous increase in the population have led those who are engaged in plant production to seek ways to make maximum use of the existing agricultural areas. One of these ways is mixed sowing systems. It is very difficult to sow species with different grain sizes in mixtures. Special sowing machines are needed for this aim. Because of this reason, the article aims to be a guide for artificial intelligence capable of mixed sowing in forage crops. In the research, it is found that there are some differences between YOLOv4-tiny and Y0L0v4 models as Precision, Recall, F1-score, TP, FP, FN scores. For the YOLOv4-tiny model, these scores were found as 0.99, 1.00, 0.99, 90, 1, 0, respectively and the scores for the Y0L0v4 model were 1.00, 1.00, 1.00, 90, 0, 0. According to the YOLOv4-tiny and YOLOv4 tests in the lab, suggesting that the YOLOv4-tiny is faster, and the YOLOv4 is more reliable in terms of all these factors combined. This research establishes a standard for real-time recognition of forage crops based on current technology at NVIDIA Jetson TX2 due to its high performance and low power consumption and a high-performance computer with CUDA support.

Keywords:
forage crops; smart agriculture; real-time object detection; yolov4-tiny; yolov4

HIGHLIGHTS

• Farmers try to make maximum use of the existing agricultural areas.

• One of these ways is mixed sowing systems.

• It is very difficult to sow species with different grain sizes in mixtures.

• Special sowing machines are needed for this aim.

• So, the article aims to be a guide for artificial intelligence capable mixed sowing in forage crops.