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The Future of Artificial Intelligence and Sensor Systems in Agriculture
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The Future of Artificial Intelligence and Sensor Systems in Agriculture

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Modeling".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 3006

Special Issue Editors

Dr. Gerassimos Peteinatos

E-Mail Website
Guest Editor
Department of Agricultural Machinery, Soil and Water Resources Institute, ELGO - DIMITRA, 13561 Athens, Greece
Interests: precision farming; sensors in agriculture; robotics; mechanical weed management; AI; hyperspecral data; innovation technologies
Dr. Hugo Moreno

E-Mail Website
Guest Editor
Centre for Automation and Robotics, CSIC-UPM, Arganda del Rey, 28500 Madrid, Spain
Interests: site-specific weed management; assessment of crop geometry

Special Issue Information

Dear Colleagues,

This upcoming Special Issue of Plants, entitled "Future of Artificial Intelligence and Sensor Systems in Agriculture", explores the dynamic intersection between technology and farming practices. In the past few decades, agriculture has undergone a significant transformation, with the introduction of automation, and improvements in both the know-how and the flow of resources within the field. Since 2015, there was a huge expansion of half- and fully autonomous cropping systems, which began to be integrated into practical agriculture. A number of technological advancements have been made in terms of precision farming, sensor systems, and artificial intelligence (AI), enabling better crop management and resource optimization. This SI aims to provide an overview of the subject and showcase potential uses of artificial intelligence and sensor systems to transform agriculture.

Specifically, this Issue will delve into potential benefits such as increased productivity, sustainability, and reduced resource usage. Researchers are encouraged to submit scientific papers that cover various aspects of the subject, including AI-driven crop monitoring, automated machinery, soil and environmental sensors, and data analytics. Papers exploring the impact of these technologies on different agricultural sectors, from crop cultivation to plant identification and phenotyping, are highly welcomed. The purpose of this publication is ultimately to highlight the transformative role that artificial intelligence and sensors can play in securing the future of agriculture.

Dr. Gerassimos Peteinatos
Dr. Hugo Moreno
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Plants 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 2700 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

  • machine learning
  • plant phenotyping
  • sustainable agriculture
  • remote sensing
  • camera-based solutions
  • robots
  • sensor-based IPM
  • UAV
  • deep learning
  • site-specific crop management

Published Papers (3 papers)

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Research

27 pages, 7676 KiB  
Article
Application of Multimodal Transformer Model in Intelligent Agricultural Disease Detection and Question-Answering Systems
by Yuchun Lu, Xiaoyi Lu, Liping Zheng, Min Sun, Siyu Chen, Baiyan Chen, Tong Wang, Jiming Yang and Chunli Lv
Plants 2024, 13(7), 972; https://doi.org/10.3390/plants13070972 - 28 Mar 2024
Viewed by 807
Abstract
In this study, an innovative approach based on multimodal data and the transformer model was proposed to address challenges in agricultural disease detection and question-answering systems. This method effectively integrates image, text, and sensor data, utilizing deep learning technologies to profoundly analyze and [...] Read more.
In this study, an innovative approach based on multimodal data and the transformer model was proposed to address challenges in agricultural disease detection and question-answering systems. This method effectively integrates image, text, and sensor data, utilizing deep learning technologies to profoundly analyze and process complex agriculture-related issues. The study achieved technical breakthroughs and provides new perspectives and tools for the development of intelligent agriculture. In the task of agricultural disease detection, the proposed method demonstrated outstanding performance, achieving a precision, recall, and accuracy of 0.95, 0.92, and 0.94, respectively, significantly outperforming the other conventional deep learning models. These results indicate the method’s effectiveness in identifying and accurately classifying various agricultural diseases, particularly excelling in handling subtle features and complex data. In the task of generating descriptive text from agricultural images, the method also exhibited impressive performance, with a precision, recall, and accuracy of 0.92, 0.88, and 0.91, respectively. This demonstrates that the method can not only deeply understand the content of agricultural images but also generate accurate and rich descriptive texts. The object detection experiment further validated the effectiveness of our approach, where the method achieved a precision, recall, and accuracy of 0.96, 0.91, and 0.94. This achievement highlights the method’s capability for accurately locating and identifying agricultural targets, especially in complex environments. Overall, the approach in this study not only demonstrated exceptional performance in multiple tasks such as agricultural disease detection, image captioning, and object detection but also showcased the immense potential of multimodal data and deep learning technologies in the application of intelligent agriculture. Full article
(This article belongs to the Special Issue The Future of Artificial Intelligence and Sensor Systems in Agriculture)
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12 pages, 1155 KiB  
Article
Using Machine Learning Methods Combined with Vegetation Indices and Growth Indicators to Predict Seed Yield of Bromus inermis
by Chengming Ou, Zhicheng Jia, Shoujiang Sun, Jingyu Liu, Wen Ma, Juan Wang, Chunjiao Mi and Peisheng Mao
Plants 2024, 13(6), 773; https://doi.org/10.3390/plants13060773 - 8 Mar 2024
Viewed by 563
Abstract
Smooth bromegrass (Bromus inermis) is a perennial, high-quality forage grass. However, its seed yield is influenced by agronomic practices, climatic conditions, and the growing year. The rapid and effective prediction of seed yield can assist growers in making informed production decisions [...] Read more.
Smooth bromegrass (Bromus inermis) is a perennial, high-quality forage grass. However, its seed yield is influenced by agronomic practices, climatic conditions, and the growing year. The rapid and effective prediction of seed yield can assist growers in making informed production decisions and reducing agricultural risks. Our field trial design followed a completely randomized block design with four blocks and three nitrogen levels (0, 100, and 200 kg·N·ha−1) during 2022 and 2023. Data on the remote vegetation index (RVI), the normalized difference vegetation index (NDVI), the leaf nitrogen content (LNC), and the leaf area index (LAI) were collected at heading, anthesis, and milk stages. Multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) regression models were utilized to predict seed yield. In 2022, the results indicated that nitrogen application provided a sufficiently large range of variation of seed yield (ranging from 45.79 to 379.45 kg ha⁻¹). Correlation analysis showed that the indices of the RVI, the NDVI, the LNC, and the LAI in 2022 presented significant positive correlation with seed yield, and the highest correlation coefficient was observed at the heading stage. The data from 2022 were utilized to formulate a predictive model for seed yield. The results suggested that utilizing data from the heading stage produced the best prediction performance. SVM and RF outperformed MLR in prediction, with RF demonstrating the highest performance (R2 = 0.75, RMSE = 51.93 kg ha−1, MAE = 29.43 kg ha−1, and MAPE = 0.17). Notably, the accuracy of predicting seed yield for the year 2023 using this model had decreased. Feature importance analysis of the RF model revealed that LNC was a crucial indicator for predicting smooth bromegrass seed yield. Further studies with an expanded dataset and integration of weather data are needed to improve the accuracy and generalizability of the model and adaptability for the growing year. Full article
(This article belongs to the Special Issue The Future of Artificial Intelligence and Sensor Systems in Agriculture)
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17 pages, 3866 KiB  
Article
AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data
by Asma Khan, Sharaf J. Malebary, L. Minh Dang, Faisal Binzagr, Hyoung-Kyu Song and Hyeonjoon Moon
Plants 2024, 13(5), 653; https://doi.org/10.3390/plants13050653 - 27 Feb 2024
Viewed by 1112
Abstract
Our research focuses on addressing the challenge of crop diseases and pest infestations in agriculture by utilizing UAV technology for improved crop monitoring through unmanned aerial vehicles (UAVs) and enhancing the detection and classification of agricultural pests. Traditional approaches often require arduous manual [...] Read more.
Our research focuses on addressing the challenge of crop diseases and pest infestations in agriculture by utilizing UAV technology for improved crop monitoring through unmanned aerial vehicles (UAVs) and enhancing the detection and classification of agricultural pests. Traditional approaches often require arduous manual feature extraction or computationally demanding deep learning (DL) techniques. To address this, we introduce an optimized model tailored specifically for UAV-based applications. Our alterations to the YOLOv5s model, which include advanced attention modules, expanded cross-stage partial network (CSP) modules, and refined multiscale feature extraction mechanisms, enable precise pest detection and classification. Inspired by the efficiency and versatility of UAVs, our study strives to revolutionize pest management in sustainable agriculture while also detecting and preventing crop diseases. We conducted rigorous testing on a medium-scale dataset, identifying five agricultural pests, namely ants, grasshoppers, palm weevils, shield bugs, and wasps. Our comprehensive experimental analysis showcases superior performance compared to various YOLOv5 model versions. The proposed model obtained higher performance, with an average precision of 96.0%, an average recall of 93.0%, and a mean average precision (mAP) of 95.0%. Furthermore, the inherent capabilities of UAVs, combined with the YOLOv5s model tested here, could offer a reliable solution for real-time pest detection, demonstrating significant potential to optimize and improve agricultural production within a drone-centric ecosystem. Full article
(This article belongs to the Special Issue The Future of Artificial Intelligence and Sensor Systems in Agriculture)
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