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 (3 papers)

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All Agriculture AgriEngineering Agronomy Applied Sciences Automation Crops Robotics Sensors

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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 (registering DOI) - 30 Apr 2025

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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

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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

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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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