Open research questions in Smart Agriculture and AI
2,325 gap statements mined from Smart Agriculture and AI papers in our 7.9M-paper local library, which holds 6,745 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.
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What the literature leaves open
Single-model methods for automated plant disease diagnosis are not always robust or generalisable. The need for a scalable solution for precision agriculture and timely disease control. The lack of a framework that combines deep feature extraction with ensemble learning for plant disease detection.
Hybrid convolutional neural network–random forest framework for automated detection of bell pepper leaf diseases using image analysis · 2026 · DOIThe lack of efficient processing of large-scale plant image data in precision agriculture. The need for real-time monitoring and predictive modeling in smart farming systems. The limitation of existing data clustering techniques in handling large-scale IoT-generated datasets.
IoT-driven early detection of plant diseases in smart farming systems based on large-scale data · 2026 · DOIThe lack of a large, publicly available maize image dataset from Tanzania. The need for a dataset that supports the development of machine learning models for crop disease diagnosis.
It highlights the need for digital transformation in agriculture to address the challenges faced by the sector. The research gap is the lack of effective machine vision technologies for precision agriculture.
A comprehensive survey on machine vision applications in precision agriculture: current trends and future perspectives · 2026 · DOIPrior approaches to saffron stigma detection have limitations in terms of accuracy and efficiency. The YOLO framework requires improvement for application to saffron stigma detection. The need for intelligent systems capable of assisting or replacing human labor in stigma detection and harvesting.
Geometric and sentence-level semantic information transfer learning for YOLO based saffron stigma detection · 2026 · DOIIt highlights the need for standardized field verification to enhance the autonomous decision-making, precision operation, and long-term reliability of agricultural irrigation and drainage machinery.
From Automation to Intelligence: A Review of Key Technologies and Emerging Trends in Agricultural Irrigation and Drainage Machinery · 2026 · DOIThe paper identifies the gap in the current state of agricultural irrigation and drainage machinery, including limited sensing capabilities and insufficient intelligent decision-making. It highlights the need for integrated irrigation and drainage, multimodal spatiotemporal fusion, mechanism and data collaborative driving, and cloud-edge-end collaborative intelligence.
From Automation to Intelligence: A Review of Key Technologies and Emerging Trends in Agricultural Irrigation and Drainage Machinery · 2026 · DOITraditional convolutional neural networks have limitations in modeling long-range dependencies. Vision transformers often fail to maintain fine-grained local texture information. There is a need for a model that combines local and global features for accurate plant disease detection.
Hybrid local–global vision transformer framework for robust plant disease detection in precision agriculture · 2026 · DOIThe lack of smart systems for monitoring and predicting microclimates in commercial poultry farms. The need for a systematic review of IoT- and AI-enabled microclimate regulation for broilers. The importance of assessing technical issues and physiological and economic results to develop a strategy for future research.
Climate-smart precision poultry farming: artificial intelligence and sensor-based microclimate control for enhancing welfare and production efficiency · 2026 · DOIThe need to adapt to the real-world distribution density of the potato canopy. The requirement for high prediction accuracy across various density gradients based on high-fidelity masks.
A cascaded parallel deep learning framework for precise potato canopy density mapping via edge-aware feature fusion · 2026 · DOIThe lack of a technical foundation for canopy coverage evaluation in complex farmland environments. The need for a model that can adapt to the real-world distribution density of the potato canopy.
A cascaded parallel deep learning framework for precise potato canopy density mapping via edge-aware feature fusion · 2026 · DOIThe vast majority of methods are tested only on PlantVillage, a curated dataset collected in the lab. General-purpose backbones are used extensively without adaptation to the specific challenges of TLD imagery. Attention mechanisms are not co-designed with an image-enhancement stage.
Conventional CNNs generally suffer from limited interpretability and suboptimal focus on important image features. There is a need to improve the interpretability and classification accuracy of CNNs for detecting date palm diseases and pests.
Enhancing Deep Learning Models with Attention Mechanisms for Interpretable Detection of Date Palm Diseases and Pests · 2025 · DOIPrior work has focused on straightforward threshold-based control, without considering sophisticated machine learning approaches. The relationship between short-interval IoT data and advanced machine learning techniques is still unexplored.
Limited availability of skilled workers during the peak harvest period. Inefficiencies, product loss, and rising operational expenses due to manual labor.
Geometric and sentence-level semantic information transfer learning for YOLO based saffron stigma detection · 2026 · DOILimited performance consistency on small-scale agricultural datasets.
ACCM-Net: an adaptive CLAHE-CBAM-MobileNet architecture for wheat leaf disease classification · 2026 · DOIValidation of the framework under real-field agricultural conditions - Evaluation of the framework on other datasets - Investigation of the application of the framework to other plant…
Hybrid convolutional neural network–random forest framework for automated detection of bell pepper leaf diseases using image analysis · 2026 · DOIValidate this framework on different agricultural environments and different types of crops to generalize the results - Extend this framework with additional disease categories - Test it with different external datasets, agricultural environments…
IoT-driven early detection of plant diseases in smart farming systems based on large-scale data · 2026 · DOIDevelopment of machine learning models for object detection, - Advancing artificial intelligence applications in agriculture, - Image-based recognition of visible…
The dataset was relatively limited and was obtained from a restricted number of plants grown under controlled laboratory/greenhouse conditions - Nitrogen treatments were applied under controlled conditions at three predefined levels - The risk of overfitting or of model performance depending on characteristics specific to the present…
Synergistic effects of spectral preprocessing and machine learning algorithms for nitrogen estimation in tomato using hyperspectral spectral data · 2026 · DOIIndependent external validation is needed - Further research should consider a larger and more diverse dataset - The application of the proposed method under actual field conditions should be…
Synergistic effects of spectral preprocessing and machine learning algorithms for nitrogen estimation in tomato using hyperspectral spectral data · 2026 · DOINumber, experience, and assignment protocol of the raters are unknown, - Whether raters saw leaf weight when assigning grades is unknown, - Inter-rater agreement is unknown, - Camera and scale models, resolution, and calibration schedule…
Fusing conveyor-belt images with existing in-line weight records for flue-cured tobacco leaf grading: a strict date-held-out evaluation · 2026 · DOIInvestigating the use of weight sensors in other agricultural applications, - Exploring the potential of fusion pipelines in other computer vision tasks, - Developing more robust methods for combining image and weight…
Fusing conveyor-belt images with existing in-line weight records for flue-cured tobacco leaf grading: a strict date-held-out evaluation · 2026 · DOIExploring the application of GSEBNet on other crop diseases, - comparing GSEBNet with more previous methods, - using more datasets for training…
Insufficient robustness against subtle and similar early-stage symptoms. Susceptibility to background clutter and non-uniform field conditions. Reduced performance consistency on small-scale agricultural datasets.
ACCM-Net: an adaptive CLAHE-CBAM-MobileNet architecture for wheat leaf disease classification · 2026 · DOI
Most-cited papers in Smart Agriculture and AI
- Machine Learning Applications for Precision Agriculture: A Comprehensive Review · IEEE Access · 2020 · 1,007 citations
- Using deep transfer learning for image-based plant disease identification · Computers and Electronics in Agriculture · 2020 · 963 citations
- Plant Disease Detection and Classification by Deep Learning—A Review · IEEE Access · 2021 · 952 citations
- Plant diseases and pests detection based on deep learning: a review · Plant Methods · 2021 · 908 citations
- A compilation of UAV applications for precision agriculture · Computer Networks · 2020 · 877 citations
- Plant leaf disease classification using EfficientNet deep learning model · Ecological Informatics · 2020 · 867 citations
- Machine Learning in Agriculture: A Comprehensive Updated Review · Sensors · 2021 · 829 citations
- From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management · Agronomy · 2020 · 817 citations
- Decision support systems for agriculture 4.0: Survey and challenges · Computers and Electronics in Agriculture · 2020 · 781 citations
- IoT, Big Data, and Artificial Intelligence in Agriculture and Food Industry · IEEE Internet of Things Journal · 2020 · 746 citations
Most recent work
- Precision Farming with Smart Sensors: Current State, Challenges and Future Outlook · Sensors · 2026
- Sustainable agriculture practices with machine learning: implementing WKRS-RGG for plant disease detection · Environment Development and Sustainability · 2026
- Integrating UAVs, satellite remote sensing, and machine learning in precision agriculture: pathways to sustainable food production, resource efficiency, and scalable innovation · Frontiers in Agronomy · 2026
- Leveraging explainable AI for sustainable agriculture: a comprehensive review of recent advances · Artificial Intelligence Review · 2026
- A review of visual navigation for agricultural robots in open fields and controlled environments · Computers and Electronics in Agriculture · 2026
- PestScope: Exclusion-Aware Large Multimodal Model for Fine-Grained Agricultural Pest Segmentation · IEEE Transactions on Image Processing · 2026
- Agentic AI for smart and sustainable precision agriculture · Frontiers in Plant Science · 2026
- Agentic AI Framework to Automate Traditional Farming for Smart Agriculture · AgriEngineering · 2026
- Real-time on-device weed identification using a hardware-efficient lightweight CNN · Frontiers in Plant Science · 2026
- Artificial intelligence in agriculture across south Asia: Technology adoption, improvements, and sustainability outcomes · Sustainable Futures · 2026
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