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Remote Sensing
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Crop Yield Estimation Based on Remote Sensing and Artificial Intelligence
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Crop Yield Estimation Based on Remote Sensing and Artificial Intelligence

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: 15 September 2025 | Viewed by 5981

Special Issue Editors

Dr. Orly Enrique Apolo-Apolo

E-Mail Website
Guest Editor
Department of Earth & Environmental Sciences, KU Leuven, Celestijnenlaan 200E, 3001 Heverlee, Belgium
Interests: agriculture; artificial intelligence; remote-sensing; robotic; landscape; ecosystems and LiDAR
Dr. Simon Appeltans

E-Mail Website
Guest Editor
UHasselt, Data Science Institute, 3590 Diepenbeek, Belgium
Interests: agriculture; artificial intelligence; bio-engineering and biosystem engineering not elsewhere classified
Dr. Mino Sportelli

E-Mail Website
Guest Editor
Institute of Information Science and Technologies, National Research Council, 56124 Pisa, Italy
Interests: agriculture; artificial intelligence; crop protection; remote-sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Nathaniel K. Newlands

E-Mail Website
Guest Editor
Summerland Research and Development Centre, Government of Canada (Agriculture and Agri-Food Canada), Summerland, BC VOH 1Z0, Canada
Interests: sustainability metrics; crop forecasting; agroclimate; remote-sensing applications

Special Issue Information

Dear Colleagues,

Yield predictions are crucial for enabling farmers to make informed decisions in the field. Particularly valuable are those predictions that can be made well in advance of the harvest. Yield predictions involve numerous parameters pertaining to plants (e.g., fruit size, area, type of crop), weather conditions, plant systems, pruning, among others. In recent years, artificial intelligence has played a significant role in yield predictions across extensive crops, orchard crops, and horticulture. Remote sensing technologies, such as LiDAR, satellite imagery, and multispectral and hyperspectral information, have become especially important. This information can be gathered using both terrestrial and aerial platforms.

This Special Issue aims to cover all the solutions proposed by researchers for estimating crop yields, with a focus on applications in real-world agriculture. Topics may span a broad range of studies, provided they involve the use of remote sensing and artificial intelligence. Examples of topics that could be considered include the following:

  • Integration of AI and LiDAR technology for precision agriculture;
  • Advancements in satellite imagery for crop monitoring and yield estimation;
  • Utilizing multispectral and hyperspectral imaging in horticulture;
  • Machine learning models for predicting weather impact on crop yields;
  • AI-driven pest and disease detection systems for crop management;
  • Optimization of irrigation systems using remote sensing data;
  • Deep learning techniques for enhanced crop type classification;
  • Predictive analytics for soil health and its impact on crop yields;
  • Automated crop counting and size estimation using AI;
  • Impact of climate change on crop yields: AI and remote sensing approaches.

Dr. Orly Enrique Apolo-Apolo
Dr. Simon Appeltans
Dr. Mino Sportelli
Dr. Nathaniel K. Newlands
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data analytics
  • remote sensing
  • LiDAR
  • spectral analysis
  • crop modelling
  • geospatial data

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Published Papers (5 papers)

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16 pages, 2969 KiB  
Article
Enhancing Crop Yield Estimation in Spinach Crops Using Synthetic Aperture Radar-Derived Normalized Difference Vegetation Index: A Sentinel-1 and Sentinel-2 Fusion Approach
by Francisco-Javier Mesas-Carrascosa, Juan Tomás Arosemena-Jované, Susana Cantón-Martínez, Fernando Pérez-Porras and Jorge Torres-Sánchez
Remote Sens. 2025, 17(8), 1412; https://doi.org/10.3390/rs17081412 - 16 Apr 2025
Viewed by 334
Abstract
Accurate crop yield estimation is crucial for food security and effective crop management in precision agriculture. Previous studies have shown the correlation between remotely sensed data and crop yield, emphasizing the need for continuous time series of radiometric indices from satellite imagery. However, [...] Read more.
Accurate crop yield estimation is crucial for food security and effective crop management in precision agriculture. Previous studies have shown the correlation between remotely sensed data and crop yield, emphasizing the need for continuous time series of radiometric indices from satellite imagery. However, passive sensors are limited by cloud cover, restricting valid image acquisition. This study explored the integration of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data to enhance NDVI estimation and yield prediction of spinach. Random Forest Regression models were developed to predict NDVI from SAR data at two scales: (i) a general crop-scale model and (ii) specific plot-scale models. Both scales achieved R2 values above 0.9 for NDVI estimation, with better results at the plot scale. Integrating NDVI values derived from Sentinel-1 significantly improved yield estimation accuracy using NDVI time series compared to using NDVI from Sentinel-2 alone. The results indicated that plot-scale NDVI estimation had the lowest error rates (1.4%) and the highest R2 (0.89), outperforming the crop-scale model. The integration of SAR-based NDVI reduced data gaps caused by cloud cover and enabled earlier, more informed crop management decisions. These findings underscore the importance of SAR-based NDVI estimation for enhancing yield predictions in precision agriculture. Full article
(This article belongs to the Special Issue Crop Yield Estimation Based on Remote Sensing and Artificial Intelligence)
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21 pages, 12333 KiB  
Article
Geospatial Robust Wheat Yield Prediction Using Machine Learning and Integrated Crop Growth Model and Time-Series Satellite Data
by Rana Ahmad Faraz Ishaq, Guanhua Zhou, Guifei Jing, Syed Roshaan Ali Shah, Aamir Ali, Muhammad Imran, Hongzhi Jiang and Obaid-ur-Rehman
Remote Sens. 2025, 17(7), 1140; https://doi.org/10.3390/rs17071140 - 23 Mar 2025
Viewed by 666
Abstract
Accurate crop yield modeling (CYM) is inherently challenging due to the complex, nonlinear, and temporally dynamic interactions of biotic and abiotic factors. Crop traits, which historically capture the cumulative effect of these factors, exhibit functional relationships critical for optimizing productivity. This underscores the [...] Read more.
Accurate crop yield modeling (CYM) is inherently challenging due to the complex, nonlinear, and temporally dynamic interactions of biotic and abiotic factors. Crop traits, which historically capture the cumulative effect of these factors, exhibit functional relationships critical for optimizing productivity. This underscores the necessity of multi-trait-based CYM approaches. Crop growth models enable trait dynamics with reflectance data and spectral indices as proxies for crop health and traits, respectively, to have real-time, spatially explicit monitoring. The Agricultural Production Systems sIMulator was calibrated to simulate multiple traits across the growth season based on geo-tagged wheat field ground information. Reflectance and spectral indices were processed for the geo-tagged fields across temporal observations to enable real-time, spatially explicit monitoring. Based on these parameters, this study addresses a critical gap in existing CYM frameworks by proposing a machine learning-based model that synergized multiple crop traits with reflectance and spectral indices to generate site-specific yield estimates. The performance evaluation revealed that the Long Short-Term Memory (LSTM) model achieved superior accuracy for the integrated parameters (RMSE = 250.68 kg/ha, MAE = 193.76 kg/ha, and R2 = 0.84), followed by traits alone. The Random Forest model followed the LSTM model, with an RMSE = 293.56 kg/ha, MAE = 230.68 kg/ha, and R2 = 0.78 for integrated parameters, and an RMSE = 291.73 kg/ha, MAE = 223.17 kg/ha, and R2 = 0.78 for crop traits. The superior prediction demonstrated the dominant role of multiple crop traits with satellite-derived reflectance metrics to develop robust CYM frameworks capable of capturing intra- and inter-field yield variability. Full article
(This article belongs to the Special Issue Crop Yield Estimation Based on Remote Sensing and Artificial Intelligence)
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25 pages, 10324 KiB  
Article
Research for the Positioning Optimization for Portable Field Terrain Mapping Equipment Based on the Adaptive Unscented Kalman Filter Algorithm
by Jiaxing Xie, Zhenbang Yu, Gaotian Liang, Xianbing Fu, Peng Gao, Huili Yin, Daozong Sun, Weixing Wang, Yueju Xue, Jiyuan Shen and Jun Li
Remote Sens. 2024, 16(22), 4248; https://doi.org/10.3390/rs16224248 - 14 Nov 2024
Viewed by 758
Abstract
Field positioning (FP) is a key technique in the digitalization of agriculture. By integrating sensors and mapping techniques, FP can convey critical information such as soil quality, plant distribution, and topography. Utilizing vehicles for field applications provides precise control and scientific management for [...] Read more.
Field positioning (FP) is a key technique in the digitalization of agriculture. By integrating sensors and mapping techniques, FP can convey critical information such as soil quality, plant distribution, and topography. Utilizing vehicles for field applications provides precise control and scientific management for agricultural production. Compared to conventional methods, which often struggle with the complexities of field conditions and suffer from insufficient accuracy, this study employs a novel approach using self-developed multi-sensor array hardware as a portable field topographic surveying device. This innovative setup effectively navigates challenging field conditions to collect raw data. Data fusion is carried out using the Unscented Kalman Filter (UKF) algorithm. Building on this, this study combines the good point set and Opposition-based Differential Evolution for a joint improvement of the Slime Mould Algorithm. This is linked with the UKF algorithm to establish loss value feedback, realizing the adaptive parameter adjustment of the UKF algorithm. This reduces the workload of parameter setting and enhances the precision of data fusion. The improved algorithm optimizes parameters with an efficiency increase of 40.43%. Combining professional, mapping-grade total stations for accuracy comparison, the final test results show an absolute error of less than 0.3857 m, achieving decimeter-level precision in field positioning. This provides a new application technology for better implementation of agricultural digitalization. Full article
(This article belongs to the Special Issue Crop Yield Estimation Based on Remote Sensing and Artificial Intelligence)
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19 pages, 15395 KiB  
Article
The Effect of a Parcel-Aggregated Cropping Structure Mapping Method in Irrigation-Water Estimation in Arid Regions—A Case Study of the Weigan River Basin in Xinjiang
by Haoyu Wang, Linze Bai, Chunxia Wei, Junli Li, Shuo Li, Chenghu Zhou, Philippe De Maeyer, Wenqi Kou, Chi Zhang, Zhanfeng Shen and Tim Van de Voorde
Remote Sens. 2024, 16(21), 3941; https://doi.org/10.3390/rs16213941 - 23 Oct 2024
Viewed by 1080
Abstract
Effective management of agricultural water resources in arid regions relies on precise estimation of irrigation-water demand. Most previous studies have adopted pixel-level mapping methods to estimate irrigation-water demand, often leading to inaccuracies when applied in arid areas where land salinization is severe and [...] Read more.
Effective management of agricultural water resources in arid regions relies on precise estimation of irrigation-water demand. Most previous studies have adopted pixel-level mapping methods to estimate irrigation-water demand, often leading to inaccuracies when applied in arid areas where land salinization is severe and where poorly growing crops cause the growing area to be smaller than the sown area. To address this issue and improve the accuracy of irrigation-water demand estimation, this study utilizes parcel-aggregated cropping structure mapping. We conducted a case study in the Weigan River Basin, Xinjiang, China. Deep learning techniques, the Richer Convolutional Features model, and the bilayer Long Short-Term Memory model were applied to extract parcel-aggregated cropping structures. By analyzing the cropping patterns, we estimated the irrigation-water demand and calculated the supply using statistical data and the water balance approach. The results indicated that in 2020, the cultivated area in the Weigan River Basin was 5.29 × 105 hectares, distributed over 853,404 parcels with an average size of 6202 m2. Based on the parcel-aggregated cropping structure, the estimated irrigation-water demand ranges from 25.1 × 108 m3 to 30.0 × 108 m3, representing a 5.57% increase compared to the pixel-level estimates. This increase highlights the effectiveness of the parcel-aggregated cropping structure in capturing the actual irrigation-water requirements, particularly in areas with severe soil salinization and patchy crop growth. The supply was calculated at 24.4 × 108 m3 according to the water balance approach, resulting in a minimal water deficit of 0.64 × 108 m3, underscoring the challenges in managing agricultural water resources in arid regions. Overall, the use of parcel-aggregated cropping structure mapping addresses the issue of irrigation-water demand underestimation associated with pixel-level mapping in arid regions. This study provides a methodological framework for efficient agricultural water resource management and sustainable development in arid regions. Full article
(This article belongs to the Special Issue Crop Yield Estimation Based on Remote Sensing and Artificial Intelligence)
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15 pages, 4947 KiB  
Technical Note
Deep-Transfer-Learning Strategies for Crop Yield Prediction Using Climate Records and Satellite Image Time-Series Data
by Abhasha Joshi, Biswajeet Pradhan, Subrata Chakraborty, Renuganth Varatharajoo, Shilpa Gite and Abdullah Alamri
Remote Sens. 2024, 16(24), 4804; https://doi.org/10.3390/rs16244804 - 23 Dec 2024
Viewed by 1900
Abstract
The timely and reliable prediction of crop yields on a larger scale is crucial for ensuring a stable food supply and food security. In the last few years, many studies have demonstrated that deep learning can offer reliable solutions for crop yield prediction. [...] Read more.
The timely and reliable prediction of crop yields on a larger scale is crucial for ensuring a stable food supply and food security. In the last few years, many studies have demonstrated that deep learning can offer reliable solutions for crop yield prediction. However, a key challenge in applying deep-learning models to crop yield prediction is their reliance on extensive training data, which are often lacking in many parts of the world. To address this challenge, this study introduces TrAdaBoost.R2, along with fine-tuning and domain-adversarial neural network deep-transfer-learning strategies, for predicting the winter wheat yield across diverse climatic zones in the USA. All methods used the bidirectional LSTM (BiLSTM) architecture to leverage its sequential feature extraction capabilities. The proposed transfer-learning approaches outperformed the baseline deep-learning model, with mean absolute error reductions ranging from 9% to 28%, demonstrating the effectiveness of these methods. Furthermore, the results demonstrate that the semi-supervised transfer-learning approach using the two-stage version of TrAdaBoost.R2 and fine-tuning achieved a superior performance compared to the domain-adversarial neural network and standard TrAdaBoost.R2. Additionally, the study offers insights for improving the accuracy and generalizability of crop yield prediction models in diverse agricultural landscapes across different regions. Full article
(This article belongs to the Special Issue Crop Yield Estimation Based on Remote Sensing and Artificial Intelligence)
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