TL;DR
This study provides the first comprehensive benchmarking of YOLOv5 through YOLOv11 specifically for fig fruit maturity detection, demonstrating that modern YOLO architectures can effectively automate this task for robotic harvesting systems.
An automated yield prediction system using drone imagery, deep learning, and predictive analytics enables accurate yield estimation and supports improved harvest planning and decision-making and showed that manual upload provided more reliable detection than real-time analysis.
This study contributes to advancements in agricultural technology and food security by employing YOLO‐based deep learning techniques on Raspberry Pi for fruit segmentation and quality assessment. The research utilizes two object detection architectures, namely YOLOv5 and YOLOv7, to segment mandarin oranges using stat...
The tree tomato (Solanum betaceum Cav.) is a Solanaceae fruit native to South America with nutritional, functional, and economic value. However, assessing fruit maturity and making harvest-related decisions continue to rely primarily on manual visual inspection, which can be subjective and inconsistent under field cond...
Oil palm (Elaeis guineensis) is the dominant source of vegetable oil worldwide, contributing nearly 40% of global production and playing a vital role in economic development and food supply chains. Accurate estimation of fresh fruit bunches (FFB) during harvesting, known as raw fruit census, is critical for optimizing...
A smart fruit spoilage detection system that combines advanced gas sensing with deep learning for real-time monitoring and automated decision-making and enabling low-latency, real-time inference in IoT environments is presented.
Agriculture plays a crucial role in maintaining global food security, particularly in developing countries, where Moringa oleifera is a high-value crop. However, leaf diseases can reduce productivity, while manual identification remains challenging due to limited expertise. This study aims to develop an accurate and en...
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