Mathematical Problems in Engineering

Computer Vision Methods in Precision Agriculture


Publishing date
01 Mar 2022
Status
Closed
Submission deadline
22 Oct 2021

Lead Editor

1South China Agriculture University, Guangzhou, China

2The University of Tennessee, Knoxville, USA

3Northwest A&F University, Yangling, China

4Zhongkai University of Agriculture and Engineering, Guangzhou, China

5Foshan University, Foshan, China

This issue is now closed for submissions.
More articles will be published in the near future.

Computer Vision Methods in Precision Agriculture

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Computer vision is a technological application that can detect, locate, or track objects. It has been extensively studied in industrial and precision agriculture fields, particularly regarding autonomous driving, surface defect detection, object detection and localization, automatic harvesting, robotics, plant phenotyping, and crop yield estimation. Autonomous driving allows a motor vehicle to sense its environment and operate without human involvement. Uncertainties on the road such as illumination changes and occlusion make autonomous driving highly challenging. For example, pedestrian detection and lane line detection are difficult to achieve. Surface defect detection can distinguish desired features from anomalies. It is an important technology in terms of production automation. Surface defect detection requires an algorithm to detect and analyze defects quickly and robustly, which is highly challenging. Deep learning techniques can facilitate object detection and localization, but deep learning algorithms require burdensome computational processes and large amounts of storage, which poses a significant challenge in their implementation.

Mobile and fast deep learning algorithms are preferred. Automatic fruit/vegetable harvesting robots have been researched for several decades, but there is still no such commercial product available. Existing harvesting robots struggle with target fruit/vegetable detection and localization, obstacle detection and localization, collision-free path planning, vision-based servo control, and other problems. The “plant phenotype” refers to all measurable features of a plant (e.g., leaf colour and shape). The phenotype describes the relationship between the genotype and the environment of a plant’s measurable characteristics. Measuring plant phenotypes via computer vision is a notable potential research direction as it may allow for the accurate detection and segmentation of plant parts as well as the accurate reconstruction of plant parts. In addition, because nonlinear filtering is indispensable for achieving object localization and tracking, object detection under camera occlusion, as well as for fusing images from distributed cameras, nonlinear filtering methods (such as nonlinear Kalman filter, particle filter, and distributed filters) are also preferred.

The aim of this Special Issue is to bring together original research articles and review articles highlighting the application of computer vision in precision agriculture. Submissions focusing on autonomous driving, surface defect detection, object detection and localization, automatic harvesting, robotics, plant phenotyping, and crop yield estimation are particularly welcome.

Potential topics include but are not limited to the following:

  • Computer vision-based autonomous driving algorithms, which can robustly detect pedestrians, lane lines, and other targets
  • Application of generalizable real-time surface defect detection methods in precision agriculture
  • Application of end-to-end, real-time, deep neural networks that can detect and locate objects simultaneously in precision agriculture
  • Fruit/vegetable detection and localization for automatic harvesting robots (e.g., end-to-end, real-time, and precise deep neural networks that can detect target produce in a field)
  • Generalizable methods for segmenting or reconstructing plant parts to support plant phenotyping processes
  • Crop yield estimation using computer vision technology
  • Advanced nonlinear filtering methods in precision agriculture

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 5256940
  • - Research Article

Agricultural Machinery Virtual Assembly System Using Dynamic Gesture Recognitive Interaction Based on a CNN and LSTM Network

Po Zhang | Junqiang Lin | ... | Zeqin Zeng
  • Special Issue
  • - Volume 2021
  • - Article ID 3224164
  • - Research Article

Forest Farm Fire Drone Monitoring System Based on Deep Learning and Unmanned Aerial Vehicle Imagery

Shaoxiong Zheng | Weixing Wang | ... | Zepeng Wu
  • Special Issue
  • - Volume 2021
  • - Article ID 7351470
  • - Research Article

An Apple Detection Method Based on Des-YOLO v4 Algorithm for Harvesting Robots in Complex Environment

Wei Chen | Jingfeng Zhang | ... | Zhiyu Zhu
  • Special Issue
  • - Volume 2021
  • - Article ID 8555280
  • - Research Article

Grading Method of Potted Anthurium Based on RGB-D Features

Hongyu Wei | Wenqi Tang | ... | Zhiyu Ma
  • Special Issue
  • - Volume 2021
  • - Article ID 6221119
  • - Research Article

An End-to-End Learning-Based Row-Following System for an Agricultural Robot in Structured Apple Orchards

Peichen Huang | Lixue Zhu | ... | Chenyu Yang
  • Special Issue
  • - Volume 2021
  • - Article ID 7157763
  • - Research Article

Fruit Classification Utilizing a Robotic Gripper with Integrated Sensors and Adaptive Grasping

Jintao Zhang | Shuang Lai | ... | Zixuan Zhu
  • Special Issue
  • - Volume 2021
  • - Article ID 1392362
  • - Research Article

Key Points Tracking and Grooming Behavior Recognition of Bactrocera minax (Diptera: Trypetidae) via DeepLabCut

Wei Zhan | Yafeng Zou | ... | Zhiliang Zhang
  • Special Issue
  • - Volume 2021
  • - Article ID 9925940
  • - Research Article

Modelling Forest Aboveground Biomass Based on GF-3 Dual-Polarized and WorldView-3 Data: A Case Study in Datong National Wetland Park, China

Guisheng Wang | Nan Wang | Weiling Guo
  • Special Issue
  • - Volume 2021
  • - Article ID 4021426
  • - Research Article

Automated High-Resolution Structure Analysis of Plant Root with a Morphological Image Filtering Algorithm

Liang Gong | Xiaofeng Du | ... | Wanqi Liang
Mathematical Problems in Engineering
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