Crop Detection and Maturity Classification Using a YOLOv5-Based Image Analysis

Abstract

In recent years, the accurate identification of chili maturity stages has become essential for optimizing cultivation processes. Conventional methodologies, primarily reliant on manual assessments or rudimentary detection systems, often fall short of reflecting the plant’s natural environment, leading to inefficiencies and prolonged harvest periods. Such methods may be imprecise and time-consuming. With the rise of computer vision and pattern recognition technologies, new opportunities in image recognition have emerged, offering solutions to these challenges. This research proposes an affordable solution for object detection and classification, specifically through version 5 of the You Only Look Once (YOLOv5) model, to determine the location and maturity state of rocoto chili peppers cultivated in Ecuador. To enhance the model’s efficacy, we introduce a novel dataset comprising images of chili peppers in their authentic states, spanning both immature and mature stages, all while preserving their natural settings and potential environmental impediments. This methodology ensures that the dataset closely replicates real-world conditions encountered by a detection system. Upon testing the model with this dataset, it achieved an accuracy of 99.99% for the classification task and an 84% accuracy rate for the detection of the crops. These promising outcomes highlight the model’s potential, indicating a game-changing technique for chili small-scale farmers, especially in Ecuador, with prospects for broader applications in agriculture. Doi: 10.28991/ESJ-2024-08-02-08 Full Text: PDF

Plant phenotyping relevance

YOLOv5による画像解析で、トウガラシ果実の位置と成熟段階という植物形質を推定する手法を開発・評価しており、データセット構築と性能検証も中心的である。

Code and data availability

The paper's rocoto chili image dataset (1203 images, augmented to 2881) and YOLOv5 model are paper-specific phenotyping assets, but the Data Availability Statement says they are available only on request from the corresponding author, with no public URL or repository provided. No public code or model deposit is stated.

No evidence-backed public reproduction asset is currently recorded.