Role of Artificial Intelligence in Agriculture: An Analysis and Advancements With Focus on Plant Diseases

被引:14
作者
Rani, Ruchi [1 ,2 ]
Sahoo, Jayakrushna [1 ]
Bellamkonda, Sivaiah [1 ]
Kumar, Sumit [3 ]
Pippal, Sanjeev Kumar [4 ]
机构
[1] Indian Inst Informat Technol, Dept Comp Sci, Kottayam 686635, Kerala, India
[2] Dr Vishwanath Karad MIT World Peace Univ, Sch Comp Engn & Technol, Dept Comp Engn & Technol, Pune 411038, Maharashtra, India
[3] Symbiosis Int, Symbiosis Inst Technol, Pune 412115, Maharashtra, India
[4] Sharda Univ, Sharda Sch Engn & Technol, Dept Comp Sci & Engn, Greater Noida 201316, Uttar Pradesh, India
基金
英国科研创新办公室;
关键词
Artificial intelligence; plant; disease; smart farming; IoT; machine learning; deep learning; self supervised learning; IDENTIFICATION; ATTENTION; FRAMEWORK; FUSION; IOT;
D O I
10.1109/ACCESS.2023.3339375
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The increased demand for food is accelerating plant diseases globally. Hence, a manual process of detection of plant diseases is almost impossible. Artificial intelligence (AI) can offer several solutions to farmers' problems. AI is facile to mitigate farmer's agriculture challenges. With the unpredictable changing climate, plants are often affected by several diseases where AI can play an important role. AI techniques such as Machine learning and deep Learning have been employed in literature to detect, predict, and design recommendation systems for plant diseases. Significant work has been done in this area in the last two decades, which can change farmer's lives in the coming years. This paper presents a systematic multi-fold survey and analysis of such work focusing on recent AI techniques developed to combat plant diseases. This article discusses various challenges faced by farmers and their AI solutions. It analyzes several applications of AI in agriculture and current trends. Recent advancements in AI for plant disease detection, like Identification Model Improvement (IMI), Few Shot Learning (FSL), Generative Adversarial Networks (GANs), and Self Supervised Learning (SSL), are also discussed in this article. Several challenges while employing AI in plant disease detection are also discussed in this article. It will serve researchers as a valuable document for further research to solve farmer's issues.
引用
收藏
页码:137999 / 138019
页数:21
相关论文
共 112 条
[1]   Automatic vegetable disease identification approach using individual lesion features [J].
Abdu, Aliyu Muhammad ;
Mokji, Musa Mohd ;
Sheikh, Usman Ullah .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2020, 176
[2]   Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification [J].
Ahmed, Sabbir ;
Hasan, Md Bakhtiar ;
Ahmed, Tasnim ;
Sony, Md Redwan Karim ;
Kabir, Md Hasanul .
IEEE ACCESS, 2022, 10 :68868-68884
[3]   Open source application for optimum irrigation and fertilization using reclaimed water in olive orchards [J].
Alcaide Zaragoza, Carmen ;
Gonzalez Perea, Rafael ;
Fernandez Garcia, Irene ;
Camacho Poyato, Emilio ;
Rodriguez Diaz, Juan Antonio .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2020, 173
[4]   Does artificial intelligence (AI) boost digital banking user satisfaction? Integration of expectation confirmation model and antecedents of artificial intelligence enabled digital banking [J].
Alnaser, Feras Mi ;
Rahi, Samar ;
Alghizzawi, Mahmoud ;
Ngah, Abdul Hafaz .
HELIYON, 2023, 9 (08)
[5]   End-to-End Deep Learning Model for Corn Leaf Disease Classification [J].
Amin, Hassan ;
Darwish, Ashraf ;
Hassanien, Aboul Ella ;
Soliman, Mona .
IEEE ACCESS, 2022, 10 :31103-31115
[6]   PLDPNet: End-to-end hybrid deep learning framework for potato leaf disease prediction [J].
Arshad, Fizzah ;
Mateen, Muhammad ;
Hayat, Shaukat ;
Wardah, Maryam ;
Al-Huda, Zaid ;
Gu, Yeong Hyeon ;
Al-antari, Mugahed A. .
ALEXANDRIA ENGINEERING JOURNAL, 2023, 78 :406-418
[7]   Automated plant leaf disease detection and classification using optimal MobileNet based convolutional neural networks [J].
Ashwinkumar, S. ;
Rajagopal, S. ;
Manimaran, V ;
Jegajothi, B. .
MATERIALS TODAY-PROCEEDINGS, 2022, 51 :480-487
[8]   Disease Detection in Apple Leaves Using Deep Convolutional Neural Network [J].
Bansal, Prakhar ;
Kumar, Rahul ;
Kumar, Somesh .
AGRICULTURE-BASEL, 2021, 11 (07)
[9]   A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework [J].
Bari, Bifta Sama ;
Islam, Md Nahidul ;
Rashid, Mamunur ;
Hasan, Md Jahid ;
Razman, Mohd Azraai Mohd ;
Musa, Rabiu Muazu ;
Ab Nasir, Ahmad Fakhri ;
Majeed, Anwar P. P. Abdul .
PEERJ COMPUTER SCIENCE, 2021,
[10]   Smart Farming: Pomegranate Disease Detection Using Image Processing [J].
Bhange, Manisha ;
Hingoliwala, H. A. .
SECOND INTERNATIONAL SYMPOSIUM ON COMPUTER VISION AND THE INTERNET (VISIONNET'15), 2015, 58 :280-288