From bench to field: A systematic review of computer vision for tomato detection in precision agriculture (2018–2025)

Philippe Lyonel Mbouembe Touko; Guoxu Liu

doi:10.59400/cai4296

From bench to field: A systematic review of computer vision for tomato detection in precision agriculture (2018–2025)

Philippe Lyonel Mbouembe Touko
eXsolIT Research Center, Yangsan 50611, Republic of Korea
Guoxu Liu
School of Computer Engineering, Weifang University, Weifang 261061, China

Article ID: 4296

Keywords: YOLO, tomato detection, precision agriculture, object detection, systematic review, edge computing; agricultural robotics, model deployment

Abstract

Accurate tomato detection enables robotic harvesting, crop yield estimation, and tomato quality control, among other agricultural tasks. Despite remarkable advances in computer vision, particularly YOLO models, a significant gap persists between laboratory research and field deployment. This PRISMA-guided review of 110 publications (2018-2025) analyzes the disparity between lab-tested model performance and reliable real-world performance in this context. To quantify current capabilities, we construct a taxonomy based on the sensing platform, task, and environment. Across the reviewed literature, the mean average precision has increased from 78.3% for YOLOv3 to 94.7% for YOLOv11, while a controlled benchmarking study on an identical dataset (LaboroTomato) reveals smaller differences. However, tomato detection performance significantly drops in deployment, with a mean cross-domain performance loss of 8.24% due to occlusion, illumination changes, and weather conditions. Our reproducibility audit shows that most research lacks protocols for model development and that 12% releases make their public code available. Finally, 73% of high-accuracy models have requirements above the popular edge-device sizes commonly used in agricultural robotics. To bridge this implementation gap, we outline: 1) reporting guidelines to promote reproducibility, 2) decision frameworks to translate pragmatic agricultural considerations into concrete technical specifications, and 3) open research directions centered on reliability, cross-domain validation, and real-world deployment. This survey will support practitioners in agriculture, robotics, and machine learning design, deployable computer vision systems for tomatoes and other crops.

Published

2025-12-15

How to Cite

Touko, P. L. M., & Liu, G. (2025). From bench to field: A systematic review of computer vision for tomato detection in precision agriculture (2018–2025). Computing and Artificial Intelligence, 3(4). https://doi.org/10.59400/cai4296

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Issue

Vol. 3 No. 4 (2025)

Section

Review

This work is licensed under a Creative Commons Attribution 4.0 International License.

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Featured Articles

Identifying voices using convolution neural network models AlexNet and ResNet

Deep learning (DL) techniques which implement deep neural networks became popular due to the increase of high-performance computing facilities. DL achieves higher power and flexibility due to its ability to process many features when it deals with unstructured data. DL algorithm passes the data through several layers; each layer is capable of extracting features progressively and passes it to the next layer. Initial layers extract low-level features, and succeeding layers combine features to form a complete representation. This research attempts to utilize DL techniques for identifying sounds. The development in DL models has extensively covered classification and verification of objects through images. However, there have not been any notable findings concerning identification and verification of the voice of an individual from different other individuals using DL techniques. Hence, the proposed research aims to develop DL techniques capable of isolating the voice of an individual from a group of other sounds and classify them based on the use of convolutional neural networks models AlexNet and ResNet, that are used in voice identification. We achieved the classification accuracy of ResNet and AlexNet model for the problem of voice identification is 97.2039 % and 65.95% respectively, in which ResNet model achieves the best result.

Revolutionizing Neurosurgery and Neurology: The transformative impact of artificial intelligence in healthcare

The integration of artificial intelligence (AI) has brought about a paradigm shift in the landscape of Neurosurgery and Neurology, revolutionizing various facets of healthcare. This article meticulously explores seven pivotal dimensions where AI has made a substantial impact, reshaping the contours of patient care, diagnostics, and treatment modalities. AI’s exceptional precision in deciphering intricate medical imaging data expedites accurate diagnoses of neurological conditions. Harnessing patient-specific data and genetic information, AI facilitates the formulation of highly personalized treatment plans, promising more efficacious therapeutic interventions. The deployment of AI-powered robotic systems in neurosurgical procedures not only ensures surgical precision but also introduces remote capabilities, mitigating the potential for human error. Machine learning models, a core component of AI, play a crucial role in predicting disease progression, optimizing resource allocation, and elevating the overall quality of patient care. Wearable devices integrated with AI provide continuous monitoring of neurological parameters, empowering early intervention strategies for chronic conditions. AI’s prowess extends to drug discovery by scrutinizing extensive datasets, offering the prospect of groundbreaking therapies for neurological disorders. The realm of patient engagement witnesses a transformative impact through AI-driven chatbots and virtual assistants, fostering increased adherence to treatment plans. Looking ahead, the horizon of AI in Neurosurgery and Neurology holds promises of heightened personalization, augmented decision-making, early intervention, and the emergence of innovative treatment modalities. This narrative is one of optimism and collaboration, depicting a synergistic partnership between AI and healthcare professionals to propel the field forward and significantly enhance the lives of individuals grappling with neurological challenges. This article provides an encompassing view of AI’s transformative influence in Neurosurgery and Neurology, highlighting its potential to redefine the landscape of patient care and outcomes.

Enhancing user experience in large language models through human-centered design: Integrating theoretical insights with an experimental study to meet diverse software learning needs with a single document knowledge base

This paper begins with a theoretical exploration of the rise of large language models (LLMs) in Human-Computer Interaction (HCI), their impact on user experience (HX) and related challenges. It then discusses the benefits of Human-Centered Design (HCD) principles and the possibility of their application within LLMs, subsequently deriving six specific HCD guidelines for LLMs. Following this, a preliminary experiment is presented as an example to demonstrate how HCD principles can be employed to enhance user experience within GPT by using a single document input to GPT’s Knowledge base as new knowledge resource to control the interactions between GPT and users, aiming to meet the diverse needs of hypothetical software learners as much as possible. The experimental results demonstrate the effect of different elements’ forms and organizational methods in the document, as well as GPT’s relevant configurations, on the interaction effectiveness between GPT and software learners. A series of trials are conducted to explore better methods to realize text and image displaying, and jump action. Two template documents are compared in the aspects of the performances of the four interaction modes. Through continuous optimization, an improved version of the document was obtained to serve as a template for future use and research.

Clustering data analytics of urban land use for change detection

In this study, the author proposes and details a workflow for the spatial-temporal demarcation of urban areal features in 8 cities of Tamilnadu, India. During the inception phase, functional requirements and non-functional parameters are analyzed and designed, within a suitable pixel area and object-oriented derived paradigm. Land use categories are defined from OpenStreetMap (OSM) related works with the scope of conducting climate change, using multispectral sensors onboard Landsat series. Furthermore, we augment the bands dataset with Spatially Invariant Feature Transform (SIFT), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Leaf Area Index (LAI), and Texture base indices, as a means of spatially integrating auto-covariance to stationarity patterns. In doing so, change detection can be pursuit by scaling up the segmentation of regional/zonal boundaries in a multi-dimensional environment, with the aid of Wide Area Networks (WAN) cluster computers such as the BEOWULF/Google Earth Engine clusters. GeoAnalytical measures are analyzed in the design of local and zonal spatial models (GRID, RASTER, DEM, IMAGE COLLECTION). Finally, multi variate geostatistical works are ensued for precision and recall in predictive data analytics. The author proposes reusing machine learning tools (filtering by attribute-based indexing in PaaS clouds) for pattern recognition and visualization of features and feature collection.

Application of computer vision in livestock and crop production—A review

Nowadays, it is a challenge for farmers to produce healthier food for the world population and save land resources. Recently, the integration of computer vision technology in field and crop production ushered in a new era of innovation and efficiency. Computer vision, a subfield of artificial intelligence, leverages image and video analysis to extract meaningful information from visual data. In agriculture, this technology is being utilized for tasks ranging from disease detection and yield prediction to animal health monitoring and quality control. By employing various imaging techniques, such as drones, satellites, and specialized cameras, computer vision systems are able to assess the health and growth of crops and livestock with unprecedented accuracy. The review is divided into two parts: Livestock and Crop Production giving the overview of the application of computer vision applications within agriculture, highlighting its role in optimizing farming practices and enhancing agricultural productivity.