Predicting Crop Yield Using Deep Learning and Remote Sensing
Jasmin Praful Bharadiya, Nikolaos Tzenios Tzenios, Manjunath Reddy
Journal of Engineering Research and Reports · pp. 29–44 · Published 15 Apr 2023
10.9734/jerr/2023/v24i12858Abstract
The art of predicting crop production is done before the crop is harvested. Crop output forecasts will help people make timely judgments concerning food policy, prices in markets, import and export laws, and acceptable warehousing. It is possible to reduce the socioeconomic effects of crop loss brought on by a natural disaster, such as a flood or a drought, and to organize humanitarian food assistance. It has been suggested that deep learning, which lets the model to automatically extricate features and learn from the datasets, could be useful for predicting agricultural yields. This review helps to understand that how vegetation indices and environmental variables affect agricultural output by revealing gaps in our understanding of deep learning methodologies and remote sensing data in a specific area. Literature review of 2011-2022 has been collected from different databases and sites and analyzed to meet the aims of this review. The study mainly focused on the benefits of machine learning, and remote sensing for forecasting crop yield. The most often employed form of remote sensing is satellite technology, namely the usage of the Moderate-Resolution Imaging Spectro radiometer. Vegetation indices referred to as the most often employed attribute for forecasting crop yield, according to the results. This review compares all these techniques and pros and cons related to them.
Cited by 84
Susovan Kumar Pan, Ghorpade Bipin Shivaj · SHS Web of Conferences · 2025
Md Sabbir Hossain, Mostafijur Rahman, Ashifur Rahman · IEEE Access · 2025
F. Imtiaz, A. Farooque, Gurjit S. Randhawa · Computers and Electronics in Agriculture · 2025
M. Shamshad, Falak Tuba, Prabhu Kumar Yadala · AIP Conference Proceedings · 2025
Y.-M. Wang · Applied Ecology and Environmental Research · 2025
Purnima Awasthi, Sumita Mishra, Nishu Gupta · IEEE Access · 2025
Hyeon-beom Choi, Kwon-Hee Han, Jeongwook Seo · IEEE Access · 2024
J. Bharadiya · 2023
R. Ridwana, Muhammad Kamal, S. Arjasakusuma
C. Bala Kamatchi, A. Muthukumaravel · Communications in Computer and Information Science · 2025
Related research
- The Impact/Role of Artificial Intelligence in Anesthesia: Remote Pre-Operative Assessment and Perioperative — shares topic coverage
- Detecting Dental Caries through Captured Images Using the Machine Learning Technology Teachable Machine — shares topic coverage
- Harnessing Artificial Intelligence in Healthcare Analytics: From Diagnosis to Treatment Optimization — shares topic coverage
- Diagnostic Accuracy of Artificial Intelligence for Breast Cancer Detection: A Systematic Review — shares topic coverage
- Artificial Intelligence in the Analysis of the Fetal Genome in Utero: A Critical Review of Current Paradigms, Clinical Utility and Future Horizons — shares topic coverage
Article metrics
Real usage data collected on this platform.
0
Page views
0
PDF downloads
0
Outbound clicks
84
Citations
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
No views recorded yet.
Traffic sources
Referring site, by host.
No traffic recorded yet.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.