Applications of Artificial Intelligence Models and Spatiotemporal Data in Agriculture and the Ecological Environment

Topic Information

Dear Colleagues,

Agriculture, natural disasters, and ecological environment management are critical areas linked to sustainable development and human well-being. The integration of artificial intelligence (AI) models with spatiotemporal data (SD) has emerged as a transformative approach, providing powerful tools for data collection, analysis, and decision-making in these fields. This Topic aims to highlight the latest advancements and applications of AI combined with SD, showcasing how these technologies can enhance our understanding and management of agricultural systems, mitigate the impacts of natural disasters, and protect ecological environments. Traditional methods in the fields of agriculture, disaster management, and ecological monitoring often involve complex spatial and temporal data, making them time-consuming and resource-intensive. The advent of AI models combined with SD has provided researchers and practitioners with the ability to collect, process, and analyze large volumes of data efficiently. These technologies assist with the accurate and timely monitoring of agricultural processes, prediction of natural disasters, and assessment of environmental conditions. This Topic seeks to gather cutting-edge research that demonstrates the innovative applications of AI combined with SD related to agriculture, natural disasters, and ecological environment management. We aim to cover a broad spectrum of topics, including but not limited to the following:

1. Agricultural Optimization and Sustainability:

• Development and validation of AI models for crop yield prediction using SD.
• Integration of AI for improved irrigation and fertilization management.
• Assessment of climate change impacts on agricultural productivity using long-term spatiotemporal datasets.

2. Natural Disaster Management:

• Real-time monitoring and prediction of natural disasters (e.g., floods, earthquakes, landslides) using AI and SD.
• Development of early warning systems for natural disasters using integrated AI approaches.
• Post-disaster assessment and recovery planning with AI and SD.

3. Ecological Environment Monitoring:

• Assessment and mapping of ecological environments using AI and SD.
• Monitoring biodiversity and ecosystem health through AI-driven analysis of satellite imagery and sensor data.
• Prediction of environmental changes and their impact on ecosystems using AI models.

4. Urbanization and Land Use Change：

• Intelligent mapping and analysis of urban expansion and land use dynamics using AI and SD.
• Identification and classification of urban functional zones, impervious surfaces, and built-up areas using deep learning techniques.
• Scenario-based simulation and prediction of future urban land use changes driven by AI-integrated cellular automata and spatial models.

We invite researchers, practitioners, and scholars to submit original research articles, review papers, and case studies that highlight the applications of AI combined with SD in the fields agriculture, natural disasters, and ecological environment management. Submissions should provide clear evidence of the use of these technologies to address specific challenges in the field, demonstrate innovative methodologies, and present significant findings that advance the state of the art. The integration of AI and SD offers unparalleled opportunities to enhance our understanding and management of agricultural, disaster, and ecological systems. This Topic will serve as a platform for sharing the latest advancements and fostering collaboration among researchers and practitioners in this dynamic field. By showcasing innovative applications and methodologies, we hope to contribute to the development of more effective strategies for sustainable development and disaster mitigation. We look forward to receiving your contributions and to the exciting advancements that this Topic will bring to the fields of agriculture, natural disasters, and ecological environment management.

Dr. Heng Lu
Dr. Xiaoai Dai
Dr. Lei Ma
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agriculture agriculture	4.5	7.8	2011	17.4 Days	CHF 2600	Submit
Data data	2.4	5.4	2016	19.2 Days	CHF 1600	Submit
Earth earth	4.0	5.3	2020	19 Days	CHF 1400	Submit
Geomatics geomatics	3.7	4.6	2021	21.6 Days	CHF 1200	Submit
ISPRS International Journal of Geo-Information ijgi	3.2	6.7	2012	34.9 Days	CHF 1900	Submit
Land land	3.5	6.4	2012	16.4 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.3	9.4	2009	22 Days	CHF 2700	Submit
Sustainability sustainability	4.1	8.9	2009	16.9 Days	CHF 2400	Submit

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Published Papers (10 papers)

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All Agriculture Geomatics IJGI Land Sustainability Data Remote Sensing Earth

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26 pages, 1860 KB  

Open AccessData Descriptor

Topoclimatic Graph Dataset for Frost Prediction in the Tropical High-Mountain Altiplano Cundiboyacense, Colombia

by Evelin Calderón Caro, Dario Antonio Castañeda Sánchez, John R. Ballesteros and John W. Branch-Bedoya

Data 2026, 11(8), 205; https://doi.org/10.3390/data11080205 - 11 Aug 2026

Viewed by 352

Abstract

Frost prediction in tropical high-mountain agricultural regions is difficult because sparse meteorological networks must represent strong terrain-driven microclimatic variability. This article presents a topoclimatic graph dataset for frost prediction in the Altiplano Cundiboyacense, Colombia. The released core dataset contains 23 agricultural weather stations [...] Read more.

Frost prediction in tropical high-mountain agricultural regions is difficult because sparse meteorological networks must represent strong terrain-driven microclimatic variability. This article presents a topoclimatic graph dataset for frost prediction in the Altiplano Cundiboyacense, Colombia. The released core dataset contains 23 agricultural weather stations and is seasonally focused on recurrent November–February frost periods rather than year-round continuous monitoring. It includes four consistently available meteorological variables at 30 min resolution: air temperature, relative humidity, dew point temperature, and solar radiation. The data were consolidated from multiple operational sources, harmonized to a common temporal grid, subjected to physical and consistency-based quality control, and completed through temporal and spatial reconstruction with traceability labels. The final release also provides binary frost_event and frost_warning_6h labels, point-based topographic descriptors, 1 km buffer-based raster summaries, land-cover proportions, station-level static feature vectors, and graph products including edge lists and adjacency matrices. These data products support graph-based deep learning, multimodal spatiotemporal analysis, and frost early warning experiments in a tropical mountain agroecosystem. The dataset offers a reproducible framework for integrating heterogeneous environmental observations into graph-ready representations while preserving sufficient environmental context for benchmarking frost prediction methods in data-sparse regions. Full article

(This article belongs to the Topic Applications of Artificial Intelligence Models and Spatiotemporal Data in Agriculture and the Ecological Environment)

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23 pages, 6923 KB  

Open AccessArticle

Fast-YOLO11n: A Lightweight and Efficient Apple Detection Model for Complex Orchard Environments

by Jinan Gu, Zhongkai Shen, Juan Liu and Xinyu Jiang

Agriculture 2026, 16(16), 1697; https://doi.org/10.3390/agriculture16161697 - 7 Aug 2026

Viewed by 395

Abstract

Accurate and real-time apple detection in complex orchard environments is essential for robotic harvesting but remains challenging because of illumination variation, foliage occlusion, and limited computational resources. This study proposes Fast-YOLO11n, a lightweight detector derived from the nano variant of You Only Look [...] Read more.

Accurate and real-time apple detection in complex orchard environments is essential for robotic harvesting but remains challenging because of illumination variation, foliage occlusion, and limited computational resources. This study proposes Fast-YOLO11n, a lightweight detector derived from the nano variant of You Only Look Once 11 (YOLO11n) and integrating three complementary components. A Fast-C3k2 module based on partial convolution (PConv) reduces redundant computation while preserving cross-layer feature transmission. A focal modulation (FM) mechanism enhances target-related responses and suppresses background interference under occlusion and uneven illumination. In addition, a parallel downsampling module, termed ADown, retains local geometric details and multi-scale semantic information during downsampling. Experiments were conducted on a field-collected orchard dataset comprising 2240 images and 22,673 annotated apple instances under diverse lighting, scale, and occlusion conditions. Fast-YOLO11n achieved mean average precision values of 75.76% across intersection-over-union (IoU) thresholds of 0.50–0.95 (mAP@50–95) and 91.29% at an IoU threshold of 0.50 (mAP@50), while operating at 366.19 frames per second (FPS) with 2.51 million parameters and 6.00 billion floating-point operations (FLOPs). Compared with the YOLO11n baseline, it improved mAP@50–95 and mAP@50 by 2.39 and 1.39 percentage points, respectively, while reducing the parameter count and FLOPs by 2.71% and 5.36%. Ablation experiments demonstrated the individual and combined effects of the three modules on detection performance and computational efficiency. The proposed model provides a favorable balance between detection accuracy and computational efficiency, indicating its potential for real-time orchard perception on resource-constrained platforms. Full article

(This article belongs to the Topic Applications of Artificial Intelligence Models and Spatiotemporal Data in Agriculture and the Ecological Environment)

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25 pages, 3879 KB  

Open AccessArticle

LLM-Enabled Reconstruction of Farmer Fertilizer-Reduction Responses Under Policy Scenarios: Evidence from Sparse Stated-Preference Data

by Shuaiwen Liu, Yichuan Zhang, Zhentao Sun, Xiao Huang and Chaoqing Yu

Agriculture 2026, 16(12), 1266; https://doi.org/10.3390/agriculture16121266 - 8 Jun 2026

Cited by 1 | Viewed by 497

Abstract

Agricultural fertilizer reduction depends on farmers’ responses to policy incentives, but such responses are often observed only at a few subsidy levels and under hypothetical conditions. Using survey-based stated-preference data from 15 counties in China, this study examines whether large language model (LLM)-based [...] Read more.

Agricultural fertilizer reduction depends on farmers’ responses to policy incentives, but such responses are often observed only at a few subsidy levels and under hypothetical conditions. Using survey-based stated-preference data from 15 counties in China, this study examines whether large language model (LLM)-based methods can reconstruct fertilizer-reduction response intervals under alternative subsidy scenarios. Three LLM-based inference strategies were designed and compared with 14 conventional methods within an exploratory evaluation framework covering interval recovery, extrapolation behavior, and curve-shape plausibility. LLM-based methods were competitive in this sparse-anchor reconstruction task. The incremental inference strategy, which reconstructs target intervals through local changes between subsidy anchors, produced the most stable results. DeepSeek V3.2 Increment obtained the highest IO (0.528) and a high EIO (0.602), while Qwen3-8B Increment achieved the lowest MAME (1.291) and the highest EIO (0.636). SHAP analysis showed that reconstruction difficulty was mainly associated with fertilizer bags per mu (0.2414), annual fertilizer cost (0.1808), and fertilization training (0.1473). Overall, this study explores the potential of LLM-based inference as a flexible approach for fertilizer-reduction policy-response analysis from limited stated-preference data. Full article

(This article belongs to the Topic Applications of Artificial Intelligence Models and Spatiotemporal Data in Agriculture and the Ecological Environment)

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25 pages, 6906 KB  

Open AccessArticle

Capturing Spatial Non-Stationarity in Agricultural Land Sustainability: A Geographically Weighted Logistic Regression Approach

by Budi Siswanto, Ketut Wikantika, Albertus Deliar and Tri Muji Susantoro

ISPRS Int. J. Geo-Inf. 2026, 15(6), 253; https://doi.org/10.3390/ijgi15060253 - 5 Jun 2026

Viewed by 633

Abstract

Paddy field sustainability is essential for food security in paddy-dependent countries but is shaped by complex and spatially heterogeneous interactions among environmental, social, and economic factors. Conventional land-use models often assume spatial stationarity, limiting their ability to capture localized dynamics. This study proposes [...] Read more.

Paddy field sustainability is essential for food security in paddy-dependent countries but is shaped by complex and spatially heterogeneous interactions among environmental, social, and economic factors. Conventional land-use models often assume spatial stationarity, limiting their ability to capture localized dynamics. This study proposes a spatially explicit analytical framework by integrating Geographically Weighted Logistic Regression (GWLR) with multi-layer probabilistic surface analysis to model and classify paddy field sustainability. The framework is applied in Indramayu and Majalengka Regencies, Indonesia, using 400 stratified samples (53% paddy, 47% non-paddy) and 10,000 prediction points. Results show that GWLR outperforms global models, explaining 25.5% of deviance compared to 7.4% for logistic regression. More importantly, it reveals spatially non-stationary relationships: environmental variables exhibit relatively continuous effects, while social and economic variables show strong local heterogeneity. By transforming local coefficients into integrated probability surfaces, this study introduces a novel typology distinguishing stable paddy fields, vulnerable areas, and spatially differentiated sustainability conditions. This approach moves beyond aggregate accuracy metrics and highlights the importance of spatial context in land-use analysis. The proposed framework offers a transferable method to support place-based agricultural protection strategies in complex geographical systems. Full article

(This article belongs to the Topic Applications of Artificial Intelligence Models and Spatiotemporal Data in Agriculture and the Ecological Environment)

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19 pages, 529 KB  

Open AccessArticle

Maturity Prediction and Correlation Analysis of Additive-Treated Cattle and Sheep Manure Composts and Vermicomposts Using Machine Learning Algorithms

by Shno Karimi, Hossein Shariatmadari, Mohammad Shayannejad and Farshid Nourbakhsh

Agriculture 2026, 16(8), 834; https://doi.org/10.3390/agriculture16080834 - 9 Apr 2026

Viewed by 674

Abstract

Accurate prediction of compost maturity is vital for ensuring quality, safety, minimum substrate weight loss and agronomic performance of compost products. In this study, eight supervised machine learning (ML) classification models including Random Forest, Logistic Regression, Decision Tree, Gaussian and Multinomial Naive Bayes, [...] Read more.

Accurate prediction of compost maturity is vital for ensuring quality, safety, minimum substrate weight loss and agronomic performance of compost products. In this study, eight supervised machine learning (ML) classification models including Random Forest, Logistic Regression, Decision Tree, Gaussian and Multinomial Naive Bayes, K-Nearest Neighbors, Support Vector Machine, and AdaBoost were systematically evaluated for their ability to predict compost maturity using three key indicators: cation exchange capacity (CEC), carbon to nitrogen ratio (C/N), and humic acid (HA) content. A dataset comprising 756 samples (4 composting/vermicomposting systems × 7 treatments × 9 time points × 3 replicates) was generated. To reduce replicate-induced variability and ensure robust machine learning analysis, triplicates were averaged at each time point, resulting in 252 effective observations used for model development. Pearson correlation and heatmap analysis indicated strong interdependencies among CEC, HA, total nitrogen (TN) and organic matter (OM) content, confirming their collective utility in compost maturity classification. Model performance was assessed based on classification metrics (accuracy, precision, recall, F₁-score) and regression-based error indicators, including mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R²). Ensemble models, particularly RF and AdaBoost, showed the highest predictive accuracy (up to 0.98) and lowest error rates (e.g., MAE < 0.05, RMSE < 0.1, R² > 0.95) when predicting CEC and C/N-based maturity classes. HA-based predictions showed slightly lower precision and higher variance across models. Full article

(This article belongs to the Topic Applications of Artificial Intelligence Models and Spatiotemporal Data in Agriculture and the Ecological Environment)

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11 pages, 4770 KB  

Open AccessData Descriptor

Pasture Plant’s Dataset

by Rafael Curado, Pedro Gonçalves, Maria R. Marques and Mário Antunes

Data 2026, 11(3), 63; https://doi.org/10.3390/data11030063 - 19 Mar 2026

Cited by 1 | Viewed by 1410

Abstract

Identifying the plant species comprising a pasture, among other aspects, is crucial for assessing its nutritional value for grazing animals and facilitating its effective management. Traditionally, it requires labor-intensive visual inspection. Artificial Intelligence (AI) offers a solution for automatic classification, yet robust datasets [...] Read more.

Identifying the plant species comprising a pasture, among other aspects, is crucial for assessing its nutritional value for grazing animals and facilitating its effective management. Traditionally, it requires labor-intensive visual inspection. Artificial Intelligence (AI) offers a solution for automatic classification, yet robust datasets for training such models in natural, uncontrolled environments are scarce. This data descriptor presents a dataset of 741 images collected in pasture lands in the Centre of Portugal using standard cameras at a height of 50 cm. A semi-automated annotation pipeline was employed, utilizing a Faster R-CNN model followed by manual verification and refinement. The dataset contains 1744 annotations across four categories: ‘Shrubs’, ‘Grasses’, ‘Legumes’, and ‘Others’. It includes diverse morphological variations and captures real-world challenges such as occlusion and lighting variability. This dataset serves as a benchmark for training object detection models in agricultural settings, facilitating the development of automated monitoring systems for precision agriculture. Such a mechanism could be incorporated into a mobile application, mounted on a drone, or embedded in an animal-worn device, enabling automated sampling and identification of the plant composition within a pasture. Full article

(This article belongs to the Topic Applications of Artificial Intelligence Models and Spatiotemporal Data in Agriculture and the Ecological Environment)

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27 pages, 1402 KB  

Open AccessArticle

A Hybrid Secondary-Decomposition and Intelligent- Optimization Framework for Agricultural Product Price Forecasting

by Haoran Wang, Chang Su, Songsong Hou, Mengjing Jia, Qichao Tang and Yan Guo

Sustainability 2026, 18(4), 2057; https://doi.org/10.3390/su18042057 - 18 Feb 2026

Viewed by 669

Abstract

With the rapid development of big data and artificial intelligence, agricultural product price forecasting is evolving toward more intelligent and accurate approaches. However, such prices are affected by complex factors including natural conditions, market dynamics, and policy changes, resulting in strong nonlinearity and [...] Read more.

With the rapid development of big data and artificial intelligence, agricultural product price forecasting is evolving toward more intelligent and accurate approaches. However, such prices are affected by complex factors including natural conditions, market dynamics, and policy changes, resulting in strong nonlinearity and noise. To address the above challenges and achieve accurate agricultural price forecasts, this study proposes a hybrid framework that integrates a secondary decomposition algorithm with an improved Human Evolutionary Optimization Algorithm specifically tailored for the agricultural domain. The original price series is first decomposed using complete ensemble empirical mode decomposition with adaptive noise, and the high-frequency component is further processed using variational mode decomposition to enhance feature extraction. The improved optimization algorithm introduces Gaussian mutation and adaptive weights to optimize neural network parameters. Experiments on wheat, Chinese cabbage, and broiler chicken demonstrate that the proposed model significantly improves prediction accuracy, with determination coefficients increasing by 6.69, 8.87, and 6.43 percentage points, respectively. The results confirm the model’s effectiveness in reducing noise, capturing multi-scale features, and improving forecasting performance. Full article

(This article belongs to the Topic Applications of Artificial Intelligence Models and Spatiotemporal Data in Agriculture and the Ecological Environment)

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22 pages, 4095 KB  

Open AccessFeature PaperArticle

Precise Extraction of Croplands from Remote Sensing Images in Egypt by a Dual-Encoder U-Net with Multi-Scale Axial Attention and Boundary Constraints

by Yong Li, Han Ding, Heiko Balzter, Vagner Ferreira, Ying Ge, Hongyan Wang, Huiyu Zhou, Tengbo Sun, Lulu Shi, Meiyun Lai and Xiuhui Liu

Land 2026, 15(2), 305; https://doi.org/10.3390/land15020305 - 11 Feb 2026

Viewed by 1574

Abstract

Accurate cropland parcel mapping is essential for food security and sustainable land management in arid Africa, yet it remains challenging in Egypt due to edge blurring, spectral confusion, and fragmented fields in medium-resolution imagery. A novel dual-encoder deep learning method that integrates multi-scale [...] Read more.

Accurate cropland parcel mapping is essential for food security and sustainable land management in arid Africa, yet it remains challenging in Egypt due to edge blurring, spectral confusion, and fragmented fields in medium-resolution imagery. A novel dual-encoder deep learning method that integrates multi-scale axial attention and boundary constraints (MAA-BCNet) is proposed for the precise extraction of croplands in Egypt from Sentinel-2 multispectral images. A dual-path encoder is designed to fuse CNN-based local textures with an RMT global branch using spatial decay attention for complementary feature extraction. A multi-scale axial attention module is introduced to capture anisotropic parcel structures for improved spectral–spatial discrimination, and a multi-directional gradient edge enhancement module is developed for explicitly preserving boundary integrity. A U-Net++ decoder is employed for dense multi-scale aggregation. Experimental results in Egypt demonstrate that MAA-BCNet achieves superior performance in delineating cropland parcels, particularly for irregular or fragmented croplands with complex landscapes and fuzzy boundaries. Compared with the widely used segmentation models such as DeepLabV3_plus, PSPnet, Link_net, FCN_resnet101, and U-Net++ under the same training and evaluation settings, our model has the best performance, with Recall, Precision, IoU, and F1-Score reaching 94.92%, 90.77%, 86.57%, and 92.80%, respectively. These advancements make MAA-BCNet suitable for cropland mapping of large areas of Egypt, with applications in precision agriculture and sustainable land management. Full article

(This article belongs to the Topic Applications of Artificial Intelligence Models and Spatiotemporal Data in Agriculture and the Ecological Environment)

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8 pages, 3871 KB  

Open AccessData Descriptor

A Georeferenced Field Dataset of Forest Cover Density and Composition for Vegetation Classification and Monitoring

by Lucio Di Cosmo, Patrizia Gasparini, Antonio Floris, Maria Rizzo, Hannes Markart and Marco Pietrogiovanna

Data 2026, 11(1), 5; https://doi.org/10.3390/data11010005 - 1 Jan 2026

Viewed by 574

Abstract

Forests provide a wide range of ecosystem services, and their importance in supporting human well-being is widely recognized. As goods and benefits from forests are exhaustible, it is therefore essential to gather sound data for their monitoring and management. Remote sensing has gained [...] Read more.

Forests provide a wide range of ecosystem services, and their importance in supporting human well-being is widely recognized. As goods and benefits from forests are exhaustible, it is therefore essential to gather sound data for their monitoring and management. Remote sensing has gained increasing importance in collecting data on forests, driven by the growing demand for regularly updated environmental data. However, remote sensing modeling of vegetation requires reference data to be collected in the field. This article presents a dataset on tree crown cover—both total and by species—of 528 georeferenced forest plots located in the Eastern Alps, Italy, an area affected by extensive wind and snow damage and subsequent widespread damage caused by bark beetles. The characteristic species of the forest types in the dataset are widely distributed over the Eurasian continent, making the dataset potentially useful to many users and researchers studying forest biodiversity or remote sensing applications to monitor forest cover changes. Data were collected within a still ongoing project aimed at detecting crown cover changes in small forest patches. Full article

(This article belongs to the Topic Applications of Artificial Intelligence Models and Spatiotemporal Data in Agriculture and the Ecological Environment)

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33 pages, 1037 KB  

Open AccessArticle

Revitalizing Rural Heritage Through an Intergenerational Alternate Reality Game: A Mixed-Methods Study in Taiwan

by Jui-Hsiang Lee and Chien Yao Wang

Sustainability 2026, 18(1), 338; https://doi.org/10.3390/su18010338 - 29 Dec 2025

Cited by 2 | Viewed by 1829

Abstract

Taiwan’s rural regions face aging populations, digital divides, and fragmented heritage narratives that limit sustainable cultural revitalization. This study investigates how a community-based Alternate Reality Game (ARG) can integrate dispersed cultural assets in Shiding District into a coherent, immersive experience that supports intergenerational [...] Read more.

Taiwan’s rural regions face aging populations, digital divides, and fragmented heritage narratives that limit sustainable cultural revitalization. This study investigates how a community-based Alternate Reality Game (ARG) can integrate dispersed cultural assets in Shiding District into a coherent, immersive experience that supports intergenerational learning and community engagement. Drawing on ARG/transmedia narrative theory, scaffolding theory, intergenerational learning, and value co-creation, the research adopts an exploratory sequential mixed-methods design: qualitative interviews and co-design workshops inform ARG system development, followed by field implementation and pre–post evaluation with 78 participants across three age groups. The results show large improvements in user experience and immersion, while quantitative changes in cultural understanding, perceived learning support, and community engagement are modest and not consistently positive, despite rich qualitative accounts of heightened awareness of local history and community life. Participants’ narratives highlight a reciprocal scaffolding dynamic, in which younger visitors provide digital assistance and older residents contribute local knowledge, as well as strong perceptions of co-creation with community hosts. These findings suggest that a low-cost, participatory ARG can effectively reduce on-site narrative fragmentation and foster emotionally engaging, intergenerational experiences, but that deeper and more durable cultural learning effects likely require refined measurement and longer-term engagement. The study contributes an integrated design and evaluation framework for rural ARG applications and offers practical guidelines for communities and policymakers seeking inclusive, story-driven models of digital heritage revitalization. Full article

(This article belongs to the Topic Applications of Artificial Intelligence Models and Spatiotemporal Data in Agriculture and the Ecological Environment)

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