Trends and Prospect of Machine Vision Technology for Stresses and Diseases Detection in Precision Agriculture

被引：18

作者：

Shin, Jaemyung ^{[1
,2
]}

Mahmud, Md. Sultan ^{[2
]}

Rehman, Tanzeel U. U. ^{[2
,3
]}

Ravichandran, Prabahar ^{[2
,4
]}

Heung, Brandon ^{[5
]}

Chang, Young K. K. ^{[6
]}

机构：

[1] Univ Calgary, Schulich Sch Engn, Dept Biomed Engn, Calgary, AB T2N 1N4, Canada

[2] Dalhousie Univ, Fac Agr, Dept Engn, Truro, NS B2N 5E3, Canada

[3] Auburn Univ, Dept Biosyst Engn, Auburn, AL 36849 USA

[4] Dalhousie Univ, Fac Engn, Dept Mech Engn, Halifax, NS B3H 4R2, Canada

[5] Dalhousie Univ, Fac Agr, Dept Plant Food & Environm Sci, Truro, NS B2N 5E3, Canada

[6] South Dakota State Univ, Dept Agr & Biosyst Engn, Brookings, SD 57006 USA

来源：

AGRIENGINEERING | 2023年 / 5卷 / 01期

关键词：

stress; disease; machine vision; machine learning; image processing; IMAGE-ANALYSIS; WATER-STRESS; GRANULAR FERTILIZER; AUTOMATIC DETECTION; QUALITY EVALUATION; SPOT-APPLICATION; SURFACE-DEFECTS; COLOR; CLASSIFICATION; ORANGES;

D O I：

10.3390/agriengineering5010003

中图分类号：

S2 [农业工程];

学科分类号：

0828 ;

摘要：

Introducing machine vision-based automation to the agricultural sector is essential to meet the food demand of a rapidly growing population. Furthermore, extensive labor and time are required in agriculture; hence, agriculture automation is a major concern and an emerging subject. Machine vision-based automation can improve productivity and quality by reducing errors and adding flexibility to the work process. Primarily, machine vision technology has been used to develop crop production systems by detecting diseases more efficiently. This review provides a comprehensive overview of machine vision applications for stress/disease detection on crops, leaves, fruits, and vegetables with an exploration of new technology trends as well as the future expectation in precision agriculture. In conclusion, research on the advanced machine vision system is expected to develop the overall agricultural management system and provide rich recommendations and insights into decision-making for farmers.

引用

页码：20 / 39

页数：20

共 99 条

[91] Quality evaluation of apples based on surface defects: development of an automated inspection system
Throop, JA
Aneshansley, DJ
Anger, WC
Peterson, DL
[J]. POSTHARVEST BIOLOGY AND TECHNOLOGY, 2005, 36 (03) : 281 - 290
[92] Computer vision technology in agricultural automation —A review
Tian H.
Wang T.
Liu Y.
Qiao X.
Li Y.
[J]. Information Processing in Agriculture, 2020, 7 (01): : 1 - 19
[93] Application status and challenges of machine vision in plant factory-A review
Tian, Zhiwei
Ma, Wei
Yang, Qichang
Duan, Famin
[J]. INFORMATION PROCESSING IN AGRICULTURE, 2022, 9 (02) : 195 - 211
[94] Multimodal neural network with clustering-based drop for estimating plant water stress
Wakamori, Kazumasa
Mizuno, Ryosuke
Nakanishi, Gota
Mineno, Hiroshi
[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2020, 168
[95] Wang W., 2009, P 2009 AM SOC AGR BI
[96] Wu SG, 2007, 2007 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY, VOLS 1-3, P120
[97] A Deep-Learning-Based Real-Time Detector for Grape Leaf Diseases Using Improved Convolutional Neural Networks
Xie, Xiaoyue
Ma, Yuan
Liu, Bin
He, Jinrong
Li, Shuqin
Wang, Hongyan
[J]. FRONTIERS IN PLANT SCIENCE, 2020, 11
[98] Use of leaf color images to identify nitrogen and potassium deficient tomatoes
Xu, Guili
Zhang, Fengling
Shah, Syed Ghafoor
Ye, Yongqiang
Mao, Hanping
[J]. PATTERN RECOGNITION LETTERS, 2011, 32 (11) : 1584 - 1590
[99] Leaf image based cucumber disease recognition using sparse representation classification
Zhang, Shanwen
Wu, Xiaowei
You, Zhuhong
Zhang, Liqing
[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2017, 134 : 135 - 141

← 1 2 3 4 5 6 7 8 9 10 →