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Agriculture
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Applying Artificial Intelligence to Sustainable Crop Protection: Managing Pests and...
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Applying Artificial Intelligence to Sustainable Crop Protection: Managing Pests and Diseases

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

Deadline for manuscript submissions: 30 July 2026 | Viewed by 1731

Editor

Dr. József Sütő

E-Mail Website
Guest Editor
1. Department of IT Systems and Networks, University of Debrecen, 4028 Debrecen, Hungary
2. Department of IT, Eszterházy Károly Catholic University, 3300 Eger, Hungary
Interests: AI in embedded systems; AI for computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is already widely used in intelligent plant care, with AI-powered systems analyzing data from different kinds of sensors to monitor soil moisture, nutrient levels, plant growth, and the presence of pests and plant diseases in real time. Advances in sensor technology and the widespread adoption of internet-connected embedded systems (the Internet of Things) have greatly contributed to the increase in available data, enabling more sophisticated predictive models to be created in order to optimize harvesting, fertilization, and crop spraying. Of the various data types available, images stand out as they can be analyzed thoroughly using computer vision, enabling the early detection of plant diseases, pests, and stress from images of leaves and crops, and allowing for targeted and timely intervention. Overall, AI helps make plant care more efficient, sustainable, and precise, reducing resource use while improving plant health and yields. The aim of this Special Issue is to publish studies that use AI to provide new or improved solutions to problems arising in plant care.

Dr. József Sütő
Guest Editor

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

  • artificial intelligence
  • data
  • plant care
  • pest insect
  • plant disease

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Published Papers (2 papers)

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Research

19 pages, 9189 KB  
Article
Tea Pest and Disease Named Entity Recognition with Relative Position Bias and Hierarchical Mask
by Xi Liu, Chengkai Yu, Xinyu Deng, Jialin Lv, Tianchen Xie, Qi Chen, Jiali Wu, Yiran Liu, Weike Huang and Qiang Huang
Agriculture 2026, 16(12), 1295; https://doi.org/10.3390/agriculture16121295 - 12 Jun 2026
Viewed by 259
Abstract
Tea pest and disease named entity recognition (NER) faces challenges resulting from dense domain terminology, multi-granularity entity structures, and long-distance semantic dependencies. This paper proposes E-BERT-wwm-BiGRU-RAT-CRF, integrating whole-word masking E-BERT with three innovations—a trainable relative position bias matrix, a cross-layer hierarchical mask matrix, [...] Read more.
Tea pest and disease named entity recognition (NER) faces challenges resulting from dense domain terminology, multi-granularity entity structures, and long-distance semantic dependencies. This paper proposes E-BERT-wwm-BiGRU-RAT-CRF, integrating whole-word masking E-BERT with three innovations—a trainable relative position bias matrix, a cross-layer hierarchical mask matrix, and a heterogeneous multi-head attention mechanism—followed by bidirectional gated recurrent units (BiGRU), residual attention (RAT), and conditional random fields (CRF). On a self-constructed tea pest and disease corpus of over 300,000 characters across seven entity categories, the model achieves 93.67% precision, 93.07% recall, and 93.37% F1-score, outperforming the baseline by 2.73 percentage points in F1-score. Ablation experiments confirm the contribution of each module. Full article
(This article belongs to the Special Issue Applying Artificial Intelligence to Sustainable Crop Protection: Managing Pests and Diseases)
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22 pages, 14714 KB  
Article
TGL-YOLO: A Multi-Scale Feature Enhancement Method for Plant Disease Detection Based on Improved YOLO11
by Qi Wang and Zhiyu Wang
Agriculture 2026, 16(9), 947; https://doi.org/10.3390/agriculture16090947 - 25 Apr 2026
Viewed by 1086
Abstract
Plant disease detection in natural environments is significantly challenged by variations in lesion scales and interference from complicated background clutter. Nevertheless, current models often remain limited in effectively capturing multi-scale features and mitigating background interference simultaneously. To tackle these challenges, we present TGL-YOLO, [...] Read more.
Plant disease detection in natural environments is significantly challenged by variations in lesion scales and interference from complicated background clutter. Nevertheless, current models often remain limited in effectively capturing multi-scale features and mitigating background interference simultaneously. To tackle these challenges, we present TGL-YOLO, an improved detection network built on the YOLO11 framework. Methodologically, we introduce the Tri-Scale Dynamic Block (TSDBlock) to adaptively extract fine-grained features across highly variable lesion sizes. Furthermore, a Gated Pyramid Spatial Transformer (GPST) is designed to fuse cross-scale features and suppress background interference, while a Large Separable Pyramid Attention (LSPA) module expands the spatial receptive field to capture global context. Experimental results on two public datasets show that TGL-YOLO demonstrates improved performance over the YOLO11s baseline. On the PlantDoc dataset, it improves mAP50 and mAP50:95 by 4.7% and 3.7%, reaching 0.591 and 0.449, respectively. On the FieldPlant dataset, it reaches 0.793 and 0.608, yielding improvements of 2.3% and 1.9%. The proposed method demonstrates the capability to reduce missed detections and false positives caused by multi-scale lesions and environmental noise, providing a competitive and computationally viable solution for agricultural disease monitoring in natural environments. Full article
(This article belongs to the Special Issue Applying Artificial Intelligence to Sustainable Crop Protection: Managing Pests and Diseases)
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