Search
Automated Tomato Fruit Detection for Efficient Harvesting

August 26, 2023 | Plants |

Introduction: A recent collaborative study by National United University, Taiwan, and HCMC University of Technology and Education, Vietnam, addresses the need for efficient and automated fruit harvesting in the agricultural sector, emphasizing the importance of a circular economy approach.

The Study: The agricultural industry faces a significant challenge in labor-intensive and inefficient harvesting processes. To tackle this issue, the research introduces three object classification models based on Yolov5m, incorporating BoTNet, ShuffleNet, and GhostNet convolutional neural networks (CNNs). These models are designed for the automatic detection of tomato fruit.

Key Findings: The study involved training these models using 1508 normalized images representing three classes of cherry tomatoes: ripe, immature, and damaged. The results were promising, with the modified Yolov5m + BoTNet model demonstrating impressive detection accuracy. Specifically, the model achieved detection accuracy rates of 94% for ripe tomatoes, 95% for immature tomatoes, and 96% for damaged tomatoes. These outcomes signify a substantial advancement in the development of automated harvesting systems for tomato fruit.

Conclusion: The study showcases the potential of automated systems in revolutionizing the agricultural sector, particularly in the context of fruit harvesting. By efficiently detecting different tomato classes, this technology offers a sustainable solution that aligns with the principles of a circular economy, where waste recovery and resource efficiency play pivotal roles in addressing the challenges faced by the agricultural industry.

Read more: Tomato Fruit Detection Using Modified Yolov5m Model with Convolutional Neural Networks

Source

Fig. | Real-world detection results obtained using the modified-Yolov5m-BoTNet model for: (a) ripe tomatoes, (b) immature tomatoes, (c) immature and damaged tomatoes, (d) ripe tomatoes, (e) immature tomatoes, and (f) damaged and immature tomatoes.

Viewed Articles
Automated Tomato Fruit Detection for Efficient Harvesting
August 26, 2023 | Plants | Introduction: A recent collaborative study by National United University, Taiwan, and HCMC University of Technology and Education, Vietnam, addresses the need for efficient
Read More
Predicting site-specific economic optimal nitrogen rate using machine learning methods and on-farm precision experimentation
Precision Agriculture | April 20, 2023Researchers from the University of Nebraska conducted a study to improve nitrogen (N) fertilizer management in winter crops like wheat and barley. By applying the
Precise irrigation water and nitrogen management improve water and nitrogen use efficiencies under conservation agriculture in the maize-wheat systems
July 26, 2023 | Scientific Reports |  Introduction: Over a three-year field experiment aimed at addressing underground water depletion and ensuring agrifood system sustainability, researchers from Int
Soil organic matter content detection system based on high-temperature excitation principle
November 30, 2023 | Computers and Electronics in Agriculture |  Introduction: Precision agriculture involves using advanced technology to optimize crop growth, and soil organic matter for crop growth.
Development of a radiometric calibration method for multispectral images of croplands obtained with a remote-controlled aerial system
Remote Sensing | March 2, 2023 Researchers from Chonnam National University and Korea Aerospace Research Institute conducted a study focused on developing a practical and advanced calibration system f
Big Data and precision agriculture: a novel spatio-temporal semantic IoT data management framework for improved interoperability
Aril 28, 2023 | JOURNAL OF BIG DATA Researchers from Spain have developed an innovative system for managing spatial, temporal, and semantic data in precision agriculture within the realm of the Intern
TOP