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Agriculture
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Artificial Intelligence in Precision Agriculture: Applications in Crop Management
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Artificial Intelligence in Precision Agriculture: Applications in Crop Management

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Artificial Intelligence and Digital Agriculture".

Deadline for manuscript submissions: 10 October 2026 | Viewed by 3827

Special Issue Editors

Dr. Gaetano Messina

E-Mail Website
Guest Editor
Department of Agricultural Sciences, Mediterranean University of Reggio Calabria, Feo di Vito, 89122 Reggio Calabria, Italy
Interests: image processing; precision agriculture; remote sensing; uncrewed aerial vehicles (UAVs); geographic object-based image analysis (GEOBIA)
Dr. Souraya Benalia

E-Mail Website
Guest Editor
Department of Agricultural Sciences, Mediterranean University of Reggio Calabria, Feo di Vito, 89122 Reggio Calabria, Italy
Interests: agriculture; agricultural engineering; automation; computer vision; sustainability; production; agricultural mechanization
Special Issues, Collections and Topics in MDPI journals
Dr. Md Abdul Mueed Choudhury

E-Mail Website
Guest Editor Assistant
1. Department of Agricultural Sciences, Mediterranean University of Reggio Calabria, Feo di Vito, 89122 Reggio Calabria, Italy
2. Department of Agriculture, Food and Environmental Sciences, Università Politecnica delle Marche, Via Brecce Bianche 10, 60131 Ancona, Italy
Interests: geographic object-based image analysis (GEOBIA); remote sensing; urban forestry; LiDAR data; uncrewed aerial vehicles (UAVs)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is transforming the agricultural sector by introducing smarter, more efficient, and data-driven solutions. From precision farming and crop monitoring to automated machinery and predictive analytics, AI technologies help farmers make better decisions, aiming to enhance sustainability and productivity. As global food demand continues to rise, integrating AI into agriculture is becoming essential for ensuring sustainable farming practices and improving food security. This technological revolution is not only reshaping how food is produced but also paving the way for a more resilient and innovative agricultural industry.

This Special Issue will explore the different applications of AI in precision agriculture (PA), covering topics such as autonomous systems, advanced monitoring and sensing technologies, and data-driven decision-making.

The contributions will present advancements in crop monitoring, watering, planting, yield forecasting, systems for weeding and treatment, pest and disease detection, and agricultural mapping, along with technologies that aid both real-time and predictive decision-making. The aim of this Special Issue is to highlight the potential of AI in creating a more sustainable and efficient agricultural sector that can address future challenges.

In this Special Issue, we invite papers focusing on, but not limited to different AI applications in PA on annual and permanent crops, including the use of remote sensing platforms (i.e., uncrewed aerial vehicles, aircrafts and satellites), proximal sensors, robotic automation, machine learning and deep learning applications. Contributions based on multidisciplinary approaches, resulting from collaborations between researchers and practitioners, are also welcome, particularly those that highlight the impact of technological innovations in this field.

Dr. Gaetano Messina
Dr. Souraya Benalia
Guest Editors

Dr. Md Abdul Mueed Choudhury
Guest Editor Assistant

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 250 words) can be sent to the Editorial Office for assessment.

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. Agriculture 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 2600 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

  • precision agriculture (PA)
  • Artificial Intelligence (AI)
  • remote sensing (RS)
  • machine learning (ML)
  • deep learning (DL)
  • uncrewed aerial vehicles (UAVs)
  • crop management

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Published Papers (6 papers)

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Research

35 pages, 81315 KB  
Article
Tomato Pedicel Picking-Point Localization via Improved YOLOv8n-EED-Seg and RGB-D Fusion
by Liping Wu, Lilin Liu and Dongdong Teng
Agriculture 2026, 16(11), 1197; https://doi.org/10.3390/agriculture16111197 - 29 May 2026
Viewed by 158
Abstract
Accurate and rapid localization of tomato pedicel picking points presents a significant challenge for automated harvesting, due to factors such as occlusion by dense foliage, overlapping fruits, variable lighting conditions, and the slender morphology of pedicels. To address these, we propose an integrated [...] Read more.
Accurate and rapid localization of tomato pedicel picking points presents a significant challenge for automated harvesting, due to factors such as occlusion by dense foliage, overlapping fruits, variable lighting conditions, and the slender morphology of pedicels. To address these, we propose an integrated picking decision system combining enhanced instance segmentation with RGB-D fusion. In this study, a lightweight detection model named YOLOv8n-EED-seg is introduced. An optimized EfficientRep backbone is integrated to enhance computational efficiency, while the EMAttention mechanism and a refined DynamicHead module strengthen multi-scale feature representation for slender pedicels. The model further incorporates the Zhang–Suen algorithm for skeleton extraction and a large-neighborhood mean method for depth restoration, enabling precise 3D localization. Experiments are conducted on a dataset of 3310 images collected in a greenhouse environment. Compared with the baseline YOLOv8n-seg, our model improves precision, recall, F1 score, and mAP50 by 5.09%, 2.78%, 3.63%, and 4.31%, respectively. The system achieves an inference speed of 4.8 ms per frame, enabling real-time performance, while attaining a 93.88% success rate in 3D picking-point localization. Furthermore, the proposed model demonstrates superior robustness in complex environments compared with common segmentation models, effectively balancing accuracy, speed, and model complexity. This study provides a reliable technical pathway for high-precision, vision-based tomato-harvesting robots. Full article
(This article belongs to the Special Issue Artificial Intelligence in Precision Agriculture: Applications in Crop Management)
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18 pages, 3581 KB  
Article
Comparative Evaluation of Random Forest, XGBoost and Long Short-Term Memory Models for Weekly Banana Production Estimation on a Commercial Farm in Naranjal, Ecuador
by Maritza Aguirre-Munizaga, Mitchell Vásquez-Bermúdez, Jorge Hidalgo-Larrea, Yoansy García and María Avilés-Vera
Agriculture 2026, 16(11), 1182; https://doi.org/10.3390/agriculture16111182 - 28 May 2026
Viewed by 190
Abstract
Accurate estimation of weekly banana production is relevant for harvest, packing, and logistics planning at the farm level. This study compared Random Forest, XGBoost and Long Short-Term Memory (LSTM) models for estimating the number of banana boxes processed weekly on a commercial banana [...] Read more.
Accurate estimation of weekly banana production is relevant for harvest, packing, and logistics planning at the farm level. This study compared Random Forest, XGBoost and Long Short-Term Memory (LSTM) models for estimating the number of banana boxes processed weekly on a commercial banana farm in Naranjal canton, Ecuador. The dataset comprised 156 weekly records from January 2022 to December 2024 and integrated meteorological, edaphological and operational variables. Records from 2022 and 2023 were used for model training and hyperparameter selection, while the 52 weekly records from 2024 were retained as an unseen chronological hold-out test set. XGBoost achieved the best numerical performance on the 2024 hold-out set, followed closely by Random Forest, whereas LSTM showed weaker predictive performance given the available data. Bootstrap confidence intervals supported a cautious interpretation of the numerical differences between the tree-based models. Feature-importance analysis identified harvested bunches as the dominant operational predictor, followed by autoregressive production features and selected management-, soil-, and weather-related variables. Because harvested bunches are available only after the weekly harvest operation, the proposed model should be interpreted as a same-week production estimation or nowcasting tool rather than as a strict multi-week-ahead forecasting model. The augmented Dickey–Fuller and KPSS tests jointly supported treating the weekly target series as stationary for the purposes of the present modeling workflow. The results are limited to one farm and three production years; therefore, external validation across additional farms, seasons, and explicit ahead-of-time forecast horizons is required before broader deployment. Full article
(This article belongs to the Special Issue Artificial Intelligence in Precision Agriculture: Applications in Crop Management)
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43 pages, 155652 KB  
Article
D2FNet: A Lightweight Dual-Driven Texture–Semantic Fusion Network for Fine-Grained Real-Time UAV Weed–Crop Detection
by Chenghua Zhu, Siyan Wu, Xingqi Chao, Minxuan Lao, Lei Yang and Lihua Cai
Agriculture 2026, 16(10), 1067; https://doi.org/10.3390/agriculture16101067 - 13 May 2026
Viewed by 335
Abstract
Weed–crop object detection in UAV field imagery faces several significant challenges, including a large proportion of small objects, dense occlusions, similar texture appearance, and strong background interference. These challenges often lead to missed detections, localization drift, and unstable training under edge-device budget constraints. [...] Read more.
Weed–crop object detection in UAV field imagery faces several significant challenges, including a large proportion of small objects, dense occlusions, similar texture appearance, and strong background interference. These challenges often lead to missed detections, localization drift, and unstable training under edge-device budget constraints. To improve detection accuracy while maintaining a practical accuracy–efficiency trade-off in complex farmland scenes, we propose the Dual-Driven Texture–Semantic Fusion Network (D2FNet), consisting of a Texture–Semantic Backbone (TSB), an efficient operator MCF-A2C2f, a cross-scale adaptive fusion and feature redistribution module DSSA-Head, and a scale-aware reweighting block PSBL. TSB reduces discriminative ambiguity caused by similar weed–crop appearance and complex background textures; MCF-A2C2f controls the additional cost of the dual-driven design via lightweight operator substitution while largely preserving per-scale representations; DSSA-Head addresses multi-scale representation inconsistency induced by abundant small objects and large scale variation in field scenes; PSBL downweights low-quality positives by sample quality to stabilize box regression and training. Experimental results show that on the WeedCrop Image Dataset, D2FNet-n improves mAP50–95 from 36.6% to 44.1% (+7.5%) over the baseline YOLOv12-n; on the auxiliary Sesame Crop & Weed Dataset, mAP50–95 increases from 62.2% to 70.1% (+7.9%). These results indicate that D2FNet achieves stable accuracy gains under comparable parameter and computation budgets, rather than pursuing the smallest absolute model size, and shows promising cross-dataset robustness on the evaluated benchmarks. Full article
(This article belongs to the Special Issue Artificial Intelligence in Precision Agriculture: Applications in Crop Management)
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25 pages, 5654 KB  
Article
High-Resolution Wheat and Barley Yield Forecasting Using Multi-Temporal Satellite Time Series and Machine Learning
by Patricia Arizo-García, Sergio Castiñeira-Ibáñez, Enric Cruzado-Campos, Alberto San Bautista and Constanza Rubio
Agriculture 2026, 16(5), 516; https://doi.org/10.3390/agriculture16050516 - 26 Feb 2026
Cited by 1 | Viewed by 477
Abstract
High-resolution yield forecasting is essential for advancing precision agriculture and improving the sustainability of wheat and barley production. While most previous studies focus on field-scale predictions, pixel-level approaches are needed to capture intra-field variability and support site-specific management. This paper evaluates the performance [...] Read more.
High-resolution yield forecasting is essential for advancing precision agriculture and improving the sustainability of wheat and barley production. While most previous studies focus on field-scale predictions, pixel-level approaches are needed to capture intra-field variability and support site-specific management. This paper evaluates the performance of machine learning models for 10 m resolution yield prediction using multi-temporal Sentinel-2 surface reflectance data across seven major cereal-producing regions in Spain. Yield monitor data from winter wheat and barley fields collected over five growing seasons (2020–2024) were combined with spectral bands and vegetation indices. Random Forest (RF) and XGBoost (XGB) models were trained at five phenological stages expressed as days before harvest (DBH) and validated using both internal (2020–2023) and independent external (2024) datasets. Model accuracy increased as harvest approached. In external validation, RF achieved the best performance for wheat (R2 = 0.77; RMSE ≈ 697 kg · ha−1), while XGB performed best for barley (R2 = 0.86; RMSE ≈ 744 kg · ha−1). Visible, red-edge, and SWIR bands were the most informative predictors, especially during grain filling and senescence. Results demonstrate the potential of multi-temporal Sentinel-2 data and machine learning for accurate, transferable, pixel-level yield forecasting in Mediterranean cereal systems. Full article
(This article belongs to the Special Issue Artificial Intelligence in Precision Agriculture: Applications in Crop Management)
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26 pages, 9500 KB  
Article
Fusing Time-Series Harmonic Phenology and Ensemble Learning for Enhanced Paddy Rice Mapping and Driving Mechanisms Analysis in Anhui, China
by Nan Wu, Yiling Cui, Wei Zhuo, Bolong Zhang, Shichang Liu, Jun Wu, Zijie Zhao and Yicheng Wang
Agriculture 2026, 16(4), 459; https://doi.org/10.3390/agriculture16040459 - 16 Feb 2026
Viewed by 522
Abstract
Accurate and timely mapping of paddy rice is essential for agricultural management, food security, and climate-resilient policy. However, high-precision mapping remains challenging in subtropical monsoon regions due to persistent cloud cover, long revisit intervals, and striping noise, which compromise satellite data quality and [...] Read more.
Accurate and timely mapping of paddy rice is essential for agricultural management, food security, and climate-resilient policy. However, high-precision mapping remains challenging in subtropical monsoon regions due to persistent cloud cover, long revisit intervals, and striping noise, which compromise satellite data quality and availability. To address these limitations, a rice mapping framework suitable for different geographical environments was developed based on a random forest (RF) by combining time-series harmonic analysis (HANTS) with Sentinel-1 and Sentinel-2 multi-source data. To address these limitations, a rice mapping classification algorithm for different geographical environments was developed by combining Harmonic Analysis of Time Series (HANTS) with Sentinel-1/2 multi-source data. The research obtained annual maps of single-season and double-season rice in the research area from 2019 to 2024, with a spatial resolution of 10 m. The results indicated that the Sentinel-1, Sentinel-2, GEE, and HANTS algorithm can effectively support the yearly mapping of single- and double-season paddy rice in Anhui Province, China. The resultant paddy rice map has a high accuracy with overall accuracies exceeding 92% and Kappa coefficients above 0.84. HANTS effectively captures key phenological features of paddy rice, and it can especially enhance the discrimination between single- and double-season rice; compared to existing rice mapping products, the proposed approach reduces classification errors by an average of 3.92% in six major rice-producing cities, each with cultivation areas exceeding 1 million hectares; spatial correlation analysis indicates substantial heterogeneity in rice cultivation patterns across northern, central, and southern Anhui, associated with both biophysical and anthropogenic factors. These results indicate that integrating phenological data with machine learning can enhance the accuracy of long-term, high-resolution crop monitoring, and annual rice maps will offer valuable support for food security assessment, water resource management, and policy planning. Full article
(This article belongs to the Special Issue Artificial Intelligence in Precision Agriculture: Applications in Crop Management)
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24 pages, 18488 KB  
Article
AI-Driven Precision Mapping of Tea Plantations Using AlphaEarth Foundations: A Scalable Solution for Smart Agricultural Monitoring
by Wei Wang, Hao Guo, Shanfeng He, Fan Qi, Alim Samat, Dongjiao Wang and Jiayi Li
Agriculture 2026, 16(4), 412; https://doi.org/10.3390/agriculture16040412 - 11 Feb 2026
Cited by 1 | Viewed by 1151
Abstract
Accurate mapping of tea plantations in fragmented, mountainous landscapes faces challenges from spectral confusion, cloud-induced data gaps, and limited model transferability. To address these issues, this study proposes a data-driven approach leveraging 64-dimensional Google AlphaEarth Foundations (AEF) satellite embeddings as core predictive features, [...] Read more.
Accurate mapping of tea plantations in fragmented, mountainous landscapes faces challenges from spectral confusion, cloud-induced data gaps, and limited model transferability. To address these issues, this study proposes a data-driven approach leveraging 64-dimensional Google AlphaEarth Foundations (AEF) satellite embeddings as core predictive features, integrated with Sentinel-2 spectral, textural, and topographic variables. Prior to feature optimization, comparative experiments confirmed that Random Forest outperformed Gradient Boosting Trees, Classification and Regression Trees, and Support Vector Machines in stability and accuracy, serving as the core classifier. Leveraging a robust sampling strategy, this study evaluated 12 classification scenarios. Results showed that the AEF-augmented scenario achieved the best performance in Rizhao (Overall Accuracy 92.69%, Kappa 0.90), with a high Producer’s Accuracy of 97.47% that effectively minimized omission errors. SHapley Additive exPlanations (SHAP) analysis revealed the model’s physically interpretable logic: utilizing embeddings as “exclusion filters” to separate tea from non-target classes by encoding latent phenological patterns, while relying on original spectral bands to capture canopy biological signals. Crucially, the model demonstrated exceptional generalizability when transferred to the unseen Qingdao region without retraining. This study validates AEF embeddings as a robust, scalable feature representation for regional crop monitoring in label-scarce and heterogeneous environments, offering a transferable data foundation for precise agricultural inventory and sustainable development planning. Full article
(This article belongs to the Special Issue Artificial Intelligence in Precision Agriculture: Applications in Crop Management)
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