Digital Agriculture, Smart Farming and Crop Monitoring

Topic Information

Dear Colleagues,

We are pleased to announce a topic focusing on the rapidly evolving fields of Digital Agriculture, Smart Farming, and Crop Monitoring. This Topic aims to explore the latest advancements, challenges, and opportunities in leveraging digital technologies to transform agricultural practices, enhance productivity, and ensure sustainable farming systems.

Scope of the Topic:

This Topic invites original research articles, reviews, and case studies that address the following themes (but are not limited to):

• Crop Monitoring and Management:
  1. Remote sensing and satellite imaging for crop health assessment;
  2. Early detection of pests, diseases, and abiotic stresses for crops;
  3. Real-time crop monitoring and yield prediction.
• Smart Farming for Crop Production:
  1. Precision agriculture technologies for crop optimization;
  2. Smart irrigation and nutrient management systems;
  3. Decision support systems for crop management.
• Digital Innovations in Crop Science:
  1. Big data analytics for crop modeling and prediction;
  2. Crop microphenotype by innovative imaging to computational analysis;
  3. IoT-based solutions for crop monitoring and management.

Prof. Dr. Qingting Liu
Prof. Dr. Tao Wu
Dr. Zhigang Zhang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agriculture agriculture	3.3	4.9	2011	19.2 Days	CHF 2600	Submit
AgriEngineering agriengineering	3.0	4.7	2019	21.8 Days	CHF 1600	Submit
Agronomy agronomy	3.3	6.2	2011	17.6 Days	CHF 2600	Submit
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
Automation automation	-	2.9	2020	24.1 Days	CHF 1000	Submit
Crops crops	-	-	2021	22.1 Days	CHF 1000	Submit
Robotics robotics	2.9	6.7	2012	21 Days	CHF 1800	Submit
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600	Submit

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Published Papers (7 papers)

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All Agriculture AgriEngineering Agronomy Applied Sciences Automation Crops Robotics Sensors

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19 pages, 3355 KiB  

Open AccessArticle

RLDD-YOLOv11n: Research on Rice Leaf Disease Detection Based on YOLOv11

by Kui Fang, Rui Zhou, Nan Deng, Cheng Li and Xinghui Zhu

Agronomy 2025, 15(6), 1266; https://doi.org/10.3390/agronomy15061266 - 22 May 2025

Abstract

Rice disease identification plays a critical role in ensuring yield stability, enabling precise prevention and control, and promoting agricultural intelligence. However, existing approaches rely heavily on manual inspection, which is labor-intensive and inefficient. Moreover, the significant variability in disease features poses further challenges [...] Read more.

Rice disease identification plays a critical role in ensuring yield stability, enabling precise prevention and control, and promoting agricultural intelligence. However, existing approaches rely heavily on manual inspection, which is labor-intensive and inefficient. Moreover, the significant variability in disease features poses further challenges to accurate recognition. To address these issues, this paper proposes a novel rice leaf disease detection model—RLDD-YOLOv11n. First, the improved RLDD-YOLOv11n integrates the SCSABlock residual attention module into the neck layer to enhance multi-semantic information fusion, thereby improving the detection capability for small disease targets. Second, recognizing the limitations of the native upsampling module in YOLOv11n in reconstructing rice-disease-related features, the CARAFE upsampling module is incorporated. Finally, a rice leaf disease dataset focusing on three common diseases—Bacterial Blight, Rice Blast, and Brown Spot—was constructed. The experimental results demonstrate the effectiveness of the proposed improvements. RLDD-YOLOv11n achieved a mean Average Precision (mAP) of 88.3%, representing a 2.8% improvement over the baseline model. Furthermore, compared with existing mainstream lightweight YOLO models, RLDD-YOLOv11n exhibits a superior detection performance and robustness. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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21 pages, 359 KiB  

Open AccessReview

Applicability of Technological Tools for Digital Agriculture with a Focus on Estimating the Nutritional Status of Plants

by Bianca Cavalcante da Silva, Renato de Mello Prado, Cid Naudi Silva Campos, Fábio Henrique Rojo Baio, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo Teodoro and Dthenifer Cordeiro Santana

AgriEngineering 2025, 7(5), 161; https://doi.org/10.3390/agriengineering7050161 - 19 May 2025

Viewed by 317

Abstract

The global transition to a digital era is crucial for society, as most daily activities are driven by digital technologies aimed at enhancing productivity and efficiency in the production of food, fibers, and bioenergy. However, the segregation of digital techniques and equipment in [...] Read more.

The global transition to a digital era is crucial for society, as most daily activities are driven by digital technologies aimed at enhancing productivity and efficiency in the production of food, fibers, and bioenergy. However, the segregation of digital techniques and equipment in both rural and urban areas poses significant obstacles to technological efforts aimed at combating hunger, ensuring sustainable agriculture, and fostering innovations aligned with the United Nations Sustainable Development Goals (SDGs 02 and 09). Rural regions, which are often less connected to technological advancements, require digital transformation to shift from subsistence farming to market-integrated production. Recent efforts to expand digitalization in these areas have shown promising results. Digital agriculture encompasses terms such as artificial intelligence (AI), the Internet of Things (IoT), big data, and precision agriculture integrating information and communication with geospatial and satellite technologies to manage and visualize natural resources and agricultural production. This digitalization involves both internal and external property management through data analysis related to location, climate, phytosanitary status, and consumption. By utilizing sensors integrated into unmanned aerial vehicles (UAVs) and connected to mobile devices and machinery, farmers can monitor animals, soil, water, and plants, facilitating informed decision-making. An important limitation in studies on nutritional diagnostics is the lack of accuracy validation based on plant responses, particularly in terms of yield. This issue is observed even in conventional leaf tissue analysis methods. The absence of such validation raises concerns about the reliability of digital tools under real field conditions. To ensure the effectiveness of spectral reflectance-based diagnostics, it is essential to conduct additional studies in commercial fields across different regions. These studies are crucial to confirm the accuracy of these methods and to strengthen the development of digital and precision agriculture. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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27 pages, 8920 KiB  

Open AccessArticle

Advancing Rice Disease Detection in Farmland with an Enhanced YOLOv11 Algorithm

by Hongxin Teng, Yudi Wang, Wentao Li, Tao Chen and Qinghua Liu

Sensors 2025, 25(10), 3056; https://doi.org/10.3390/s25103056 - 12 May 2025

Viewed by 270

Abstract

Smart rice disease detection is a key part of intelligent agriculture. To address issues like low efficiency, poor accuracy, and high costs in traditional methods, this paper introduces an enhanced lightweight version of the YOLOv11-RD algorithm, enhancing multi-scale feature extraction through the integration [...] Read more.

Smart rice disease detection is a key part of intelligent agriculture. To address issues like low efficiency, poor accuracy, and high costs in traditional methods, this paper introduces an enhanced lightweight version of the YOLOv11-RD algorithm, enhancing multi-scale feature extraction through the integration of the enhanced LSKAC attention mechanism and the SPPF module. It also lowers computational complexity and enhances local feature capture through the C3k2-CFCGLU block. The C3k2-CSCBAM block in the neck region reduces the training overhead and boosts target learning in complex backgrounds. Additionally, a lightweight 320 × 320 LSDECD detection head improves small-object detection. Experiments on a rice disease dataset extracted from agricultural operation videos demonstrate that, compared to YOLOv11n, the algorithm improves mAP50 and mAP50-95 by 2.7% and 11.5%, respectively, while reducing the model parameters by 4.58 M and the computational load by 1.1 G. The algorithm offers significant advantages in lightweight design and real-time performance, outperforming other classical object detection algorithms and providing an optimal solution for real-time field diagnosis. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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20 pages, 3955 KiB  

Open AccessArticle

Lightweight Pepper Disease Detection Based on Improved YOLOv8n

by Yuzhu Wu, Junjie Huang, Siji Wang, Yujian Bao, Yizhe Wang, Jia Song and Wenwu Liu

AgriEngineering 2025, 7(5), 153; https://doi.org/10.3390/agriengineering7050153 - 12 May 2025

Viewed by 245

Abstract

China is the world’s largest producer of chili peppers, which occupy particularly important economic and social values in various fields such as medicine, food, and industry. However, during its production process, chili peppers are affected by pests and diseases, resulting in significant yield [...] Read more.

China is the world’s largest producer of chili peppers, which occupy particularly important economic and social values in various fields such as medicine, food, and industry. However, during its production process, chili peppers are affected by pests and diseases, resulting in significant yield reduction due to the temperature and environment. In this study, a lightweight pepper disease identification method, DD-YOLO, based on the YOLOv8n model, is proposed. First, the deformable convolutional module DCNv2 (Deformable ConvNetsv2) and the inverted residual mobile block iRMB (Inverted Residual Mobile Block) are introduced into the C2F module to improve the accuracy of the sampling range and reduce the computational amount. Secondly, the DySample sampling operator (Dynamic Sample) is integrated into the head network to reduce the amount of data and the complexity of computation. Finally, we use Large Separable Kernel Attention (LSKA) to improve the SPPF module (Spatial Pyramid Pooling Fast) to enhance the performance of multi-scale feature fusion. The experimental results show that the accuracy, recall, and average precision of the DD-YOLO model are 91.6%, 88.9%, and 94.4%, respectively. Compared with the base network YOLOv8n, it improves 6.2, 2.3, and 2.8 percentage points, respectively. The model weight is reduced by 22.6%, and the number of floating-point operations per second is improved by 11.1%. This method provides a technical basis for intensive cultivation and management of chili peppers, as well as efficiently and cost-effectively accomplishing the task of identifying chili pepper pests and diseases. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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20 pages, 29897 KiB  

Open AccessArticle

Accurate Parcel Extraction Combined with Multi-Resolution Remote Sensing Images Based on SAM

by Yong Dong, Hongyan Wang, Yuan Zhang, Xin Du, Qiangzi Li, Yueting Wang, Yunqi Shen, Sichen Zhang, Jing Xiao, Jingyuan Xu, Sifeng Yan, Shuguang Gong and Haoxuan Hu

Agriculture 2025, 15(9), 976; https://doi.org/10.3390/agriculture15090976 - 30 Apr 2025

Viewed by 338

Abstract

Accurately extracting parcels from satellite images is crucial in precision agriculture. Traditional edge detection fails in complex scenes with difficult post-processing, and deep learning models are time-consuming in terms of sample preparation and less transferable. Based on this, we designed a method combining [...] Read more.

Accurately extracting parcels from satellite images is crucial in precision agriculture. Traditional edge detection fails in complex scenes with difficult post-processing, and deep learning models are time-consuming in terms of sample preparation and less transferable. Based on this, we designed a method combining multi-resolution remote sensing images based on the Segment Anything Model (SAM). Using cropland masking, overlap prediction and post-processing, we achieved 10 m-resolution parcel extraction with SAM, with performance in plain areas comparable to existing deep learning models (P: 0.89, R: 0.91, F1: 0.91, IoU: 0.87). Notably, in hilly regions with fragmented cultivated land, our approach even outperformed these models (P: 0.88, R: 0.76, F1: 0.81, IoU: 0.69). Subsequently, the 10 m parcels results were utilized to crop the high-resolution image. Based on the histogram features and internal edge features of the parcels, used to determine whether to segment downward or not, and at the same time, by setting the adaptive parameters of SAM, sub-meter parcel extraction was finally realized. Farmland boundaries extracted from high-resolution images can more accurately characterize the actual parcels, which is meaningful for farmland production and management. This study extended the application of deep learning large models in remote sensing, and provided a simple and fast method for accurate extraction of parcels boundaries. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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23 pages, 4175 KiB  

Open AccessArticle

Detection of Leaf Miner Infestation in Chickpea Plants Using Hyperspectral Imaging in Morocco

by Mohamed Arame, Issam Meftah Kadmiri, Francois Bourzeix, Yahya Zennayi, Rachid Boulamtat and Abdelghani Chehbouni

Agronomy 2025, 15(5), 1106; https://doi.org/10.3390/agronomy15051106 - 30 Apr 2025

Viewed by 298

Abstract

This study addresses the problem of early detection of leaf miner infestations in chickpea crops, a significant agricultural challenge. It is motivated by the potential of hyperspectral imaging, once properly combined with machine learning, to enhance the accuracy of pest detection. Originality consists [...] Read more.

This study addresses the problem of early detection of leaf miner infestations in chickpea crops, a significant agricultural challenge. It is motivated by the potential of hyperspectral imaging, once properly combined with machine learning, to enhance the accuracy of pest detection. Originality consists of the application of these techniques to chickpea plants in controlled laboratory conditions using a natural infestation protocol, something not previously explored. The two major methodologies adopted in the approach are as follows: (1) spectral feature-based classification using hyperspectral data within the 400–1000 nm range, wherein a random forest classifier is trained to classify a plant as healthy or infested with eggs or larvae. Dimensionality reduction methods such as principal component analysis (PCA) and kernel principal component analysis (KPCA) were tried, and the best classification accuracies (over 80%) were achieved. (2) VI-based classification, leveraging indices associated with plant health, such as NDVI, EVI, and GNDVI. A support vector machine and random forest classifiers effectively classified healthy and infested plants based on these indices, with over 81% classification accuracies. The main objective was to design an integrated early pest detection framework using advanced imaging and machine learning techniques. Results show that both approaches have resulted in high classification accuracy, highlighting the potential of this approach in precision agriculture for timely pest management interventions. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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15 pages, 7102 KiB  

Open AccessArticle

Non-Contact Detection of Wine Grape Load Volume in Hopper During Mechanical Harvesting

by Haowei Liu, Xiu Wang, Jian Song, Mingzhou Chen, Cuiling Li and Changyuan Zhai

Agriculture 2025, 15(9), 918; https://doi.org/10.3390/agriculture15090918 - 23 Apr 2025

Viewed by 259

Abstract

Issues of poor real-time performance and low accuracy in the detection of load volume in the hopper during the mechanized harvesting of wine grapes are addressed in this study through the development of a proposed volume detection method based on ultrasonic sensors. First, [...] Read more.

Issues of poor real-time performance and low accuracy in the detection of load volume in the hopper during the mechanized harvesting of wine grapes are addressed in this study through the development of a proposed volume detection method based on ultrasonic sensors. First, the ultrasonic sensor beamwidth and detection height were determined through calibration tests. Next, a test bench was used to explore the influence of the number of ultrasonic sensors and conveying speed on the detected grape pile height. Data-based regression and hopper configuration-based geometric models correlating grape load volume with detected pile height were subsequently constructed; their accuracies were compared using test bench experiments to identify the optimal detection scheme. The regression model was more accurate than the geometric model under the considered conveying speeds with a maximum relative error of 8.0% for the former. Finally, field tests determined that the average grape load volume detection error during actual harvesting was 14.4%. Therefore, this study provides an effective solution for the detection of grape load volume in the hopper during mechanized harvesting and establishes a theoretical basis for the development of intelligent grape harvesting methods. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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