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Applied Sciences
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AI，IoT and Remote Sensing in Precision Agriculture
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AI，IoT and Remote Sensing in Precision Agriculture

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: closed (27 April 2024) | Viewed by 5253

Special Issue Editor

Prof. Dr. Antonio López-Quílez

E-Mail Website
Guest Editor
Department of Statistics and Operational Research, University of Valencia, Dr. Moliner 50, 46100 Burjassot, Spain
Interests: spatial statistics; bayesian statistics; environmental statistics; biostatistics; epidemiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The global population is expected to reach 10 billion by the end of 21st century. How a proportional increase in agricultural production can be attained to achieve food security and feed a growing population is one of the greatest challenges facing humanity. Furthermore, this goal needs to be achieved against the background of the need to maintain sustainable agricultural systems and the simultaneous challenges of climate change, resource depletion and extreme weather events. Automation of new technologies, sensors, yield monitors, the Internet of Things (IoT) and drones and robots, as well as the use of GIS methods, artificial intelligence (AI), highly structured mathematical models and big data statistical techniques, serves as the basis for a global “Digital Twin”. This conceptualization will contribute to the development of site-specific conservation and management practices to increase the income and global sustainability of agricultural systems. The spatial analysis of agricultural data is key in this context.

Satellite and aerial images, sensors and yield monitors provide information about production variability at the macro and micro scales via the processing, representation and modeling of abundant agricultural data. Spatiotemporal models seem to offer additional benefits beyond the classical, spatially explicit model. Hierarchical models can deal with complex interactions by specifying parameters that can change on several levels via the introduction of random effects.

The spread of transboundary plant pests and diseases caused by fungi, bacteria or viruses has increased significantly in recent years, causing substantial losses and impacting food security. In essence, they spread via human movement and are wind-borne or vector-borne. A wide range of environmental, climatic and socioeconomic factors underlie their spatial patterns. In addition, factors such as changes in climate, habits or land use intervene and complicate our understanding of these processes.

This Special Issue, which presents some of the most recent advances and novel approaches in the spatial analysis of agricultural data, is intended for a wide and multidisciplinary audience.

Prof. Dr. Antonio López-Quílez
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 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. Applied Sciences 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 2400 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

  • precision agriculture
  • ICT applications
  • Internet of Things (IoT)
  • GIS applications
  • remote sensing
  • spatial statistics
  • geospatial artificial intelligence
  • clustering
  • spatial prediction

Published Papers (6 papers)

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Research

29 pages, 3711 KiB  
Article
Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture
by Nisit Pukrongta, Attaphongse Taparugssanagorn and Kiattisak Sangpradit
Appl. Sci. 2024, 14(8), 3313; https://doi.org/10.3390/app14083313 - 15 Apr 2024
Viewed by 938
Abstract
This research introduces the PEnsemble 4 model, a weighted ensemble prediction model that integrates multiple individual machine learning models to achieve accurate maize yield forecasting. The model incorporates unmanned aerial vehicle (UAV) imagery and Internet of Things (IoT)-based environmental data, providing a comprehensive [...] Read more.
This research introduces the PEnsemble 4 model, a weighted ensemble prediction model that integrates multiple individual machine learning models to achieve accurate maize yield forecasting. The model incorporates unmanned aerial vehicle (UAV) imagery and Internet of Things (IoT)-based environmental data, providing a comprehensive and data-driven approach to yield prediction in maize cultivation. Considering the projected growth in global maize demand and the vulnerability of maize crops to weather conditions, improved prediction capabilities are of paramount importance. The PEnsemble 4 model addresses this need by leveraging comprehensive datasets encompassing soil attributes, nutrient composition, weather conditions, and UAV-captured vegetation imagery. By employing a combination of Huber and M estimates, the model effectively analyzes temporal patterns in vegetation indices, in particular CIre and NDRE, which serve as reliable indicators of canopy density and plant height. Notably, the PEnsemble 4 model demonstrates a remarkable accuracy rate of 91%. It advances the timeline for yield prediction from the conventional reproductive stage (R6) to the blister stage (R2), enabling earlier estimation and enhancing decision-making processes in farming operations. Moreover, the model extends its benefits beyond yield prediction, facilitating the detection of water and crop stress, as well as disease monitoring in broader agricultural contexts. By synergistically integrating IoT and machine learning technologies, the PEnsemble 4 model presents a novel and promising solution for maize yield prediction. Its application holds the potential to revolutionize crop management and protection, contributing to efficient and sustainable farming practices. Full article
(This article belongs to the Special Issue AI，IoT and Remote Sensing in Precision Agriculture)
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16 pages, 3783 KiB  
Article
Impacts of Variable Illumination and Image Background on Rice LAI Estimation Based on UAV RGB-Derived Color Indices
by Binfeng Sun, Yanda Li, Junbao Huang, Zhongsheng Cao and Xinyi Peng
Appl. Sci. 2024, 14(8), 3214; https://doi.org/10.3390/app14083214 - 11 Apr 2024
Viewed by 333
Abstract
Variations in illumination and image background present challenges for using UAV RGB imagery. Existing studies often overlook these issues, especially in rice. To separately evaluate the impacts of illumination variation and image background on rice LAI assessment, this study utilized Retinex correction and [...] Read more.
Variations in illumination and image background present challenges for using UAV RGB imagery. Existing studies often overlook these issues, especially in rice. To separately evaluate the impacts of illumination variation and image background on rice LAI assessment, this study utilized Retinex correction and image segmentation to eliminate illumination variations and background effects, and then analyzed the changes in color indices and their relationship with LAI before and after implementing these methods separately. The results indicated that both Retinex correction and image segmentation significantly enhanced the correlation between color indices and LAI at different growth stages as well as the accuracy of constructing a multivariate linear regression model separately. Our analysis confirmed the significance of accounting for variation in illumination and rice field backgrounds in LAI analysis when using UAV RGB images. Illumination variation and image background elements significantly degrade the accuracy of LAI estimation. Full article
(This article belongs to the Special Issue AI，IoT and Remote Sensing in Precision Agriculture)
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18 pages, 8346 KiB  
Article
Evaluation Model of Rice Seedling Production Line Seeding Quality Based on Deep Learning
by Yongbo Liu, Peng He, Yan Cao, Conghua Zhu and Shitao Ding
Appl. Sci. 2024, 14(7), 3098; https://doi.org/10.3390/app14073098 - 07 Apr 2024
Viewed by 361
Abstract
A critical precondition for realizing mechanized transplantation in rice cultivation is the implementation of seedling tray techniques. To augment the efficacy of seeding, a precise evaluation of the quality of rice seedling cultivation in these trays is imperative. This research centers on the [...] Read more.
A critical precondition for realizing mechanized transplantation in rice cultivation is the implementation of seedling tray techniques. To augment the efficacy of seeding, a precise evaluation of the quality of rice seedling cultivation in these trays is imperative. This research centers on the analysis of rice seedling tray images, employing deep learning as the foundational technology. The aim is to construct a computational model capable of autonomously evaluating seeding quality within the ambit of intelligent seedling cultivation processes. This study proposes a virtual grid-based image segmentation preprocessing method. It involves dividing the complete image of a rice seedling tray into several grid images. These grid images are then classified and marked using an improved ResNet50 model that integrates the SE attention mechanism with the Adam optimizer. Finally, the objective of detecting missing seeding areas is achieved by reassembling the marked grid images. The experimental results demonstrate that the improved ResNet50 model, integrating the SE attention mechanism and employing an initial learning rate of 0.01 over 50 iterations, attains a test set accuracy of 95.82%. This accuracy surpasses that of the AlexNet, DenseNet, and VGG16 models by respective margins of 4.55%, 2.07%, and 2.62%. This study introduces an innovative model for the automatic assessment of rice seeding quality. This model is capable of rapidly evaluating the seeding quality during the seedling phase; precisely identifying the locations of missing seeds in individual seedling trays; and effectively calculating the missing seed rate for each tray. Such precision in assessment is instrumental for optimizing seedling processes Full article
(This article belongs to the Special Issue AI，IoT and Remote Sensing in Precision Agriculture)
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19 pages, 7008 KiB  
Article
Accurate Prediction of Dissolved Oxygen in Perch Aquaculture Water by DE-GWO-SVR Hybrid Optimization Model
by Xingsheng Bao, Yilun Jiang, Lintong Zhang, Bo Liu, Linjie Chen, Wenqing Zhang, Lihang Xie, Xinze Liu, Fangfang Qu and Renye Wu
Appl. Sci. 2024, 14(2), 856; https://doi.org/10.3390/app14020856 - 19 Jan 2024
Viewed by 603
Abstract
In order to realize the accurate and reliable prediction of the change trend of dissolved oxygen (DO) content in California perch aquaculture water, this paper proposes a second-order hybrid optimization support vector machine (SVR) model based on Differential Evolution (DE) and Gray Wolf [...] Read more.
In order to realize the accurate and reliable prediction of the change trend of dissolved oxygen (DO) content in California perch aquaculture water, this paper proposes a second-order hybrid optimization support vector machine (SVR) model based on Differential Evolution (DE) and Gray Wolf Optimizer (GWO), shortened to DE-GWO-SVR, to predict the DO content with the characteristics of nonlinear and non-smooth water quality data. Experimentally, data for the water quality, including pH, water temperature, conductivity, salinity, total dissolved solids, and DO, were collected. Pearson’s correlation coefficient (PPMCC) was applied to explore the correlation between each water quality parameter and DO content. The optimal DE-GWO-SVR model was established and compared with models based on SVR, back-propagation neural network (BPNN), and their optimization models. The results show that the DE-GWO-SVR model proposed in this paper can effectively realize the nonlinear prediction and global optimization performance. Its R2, MSE, MAE and RMSE can be up to 0.94, 0.108, 0.2629, and 0.3293, respectively, which is better than those of other models. This research provides guidance for the efficient prediction of DO in perch aquaculture water bodies for increasing the aquaculture effectiveness and reducing the aquaculture risk, providing a new exploratory path for water quality monitoring. Full article
(This article belongs to the Special Issue AI，IoT and Remote Sensing in Precision Agriculture)
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20 pages, 1033 KiB  
Article
Enhancing Olive Phenology Prediction: Leveraging Market Basket Analysis and Weighted Metrics for Optimal Feature Group Selection
by Izar Azpiroz, Marco Quartulli and Igor G. Olaizola
Appl. Sci. 2023, 13(19), 10987; https://doi.org/10.3390/app131910987 - 05 Oct 2023
Viewed by 681
Abstract
Similarly efficient feature groups occur in prediction procedures such as Olive phenology forecasting. This study proposes a procedure that can be used to extract the most representative feature grouping from Market Basket Analysis-derived methodologies and other techniques. The computed association patterns in this [...] Read more.
Similarly efficient feature groups occur in prediction procedures such as Olive phenology forecasting. This study proposes a procedure that can be used to extract the most representative feature grouping from Market Basket Analysis-derived methodologies and other techniques. The computed association patterns in this process are visualized through graph analytic tools, comparing centrality metrics and spacial distribution approaches. Finally, highlighted feature formations are located and analyzed within the efficiency distribution of all proposed feature combinations for validation purposes. Full article
(This article belongs to the Special Issue AI，IoT and Remote Sensing in Precision Agriculture)
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13 pages, 7386 KiB  
Article
Development of Potato Mass Estimation System Based on Deep Learning
by Sung-Hyuk Jang, Seok-Pyo Moon, Yong-Joo Kim and Sang-Hee Lee
Appl. Sci. 2023, 13(4), 2614; https://doi.org/10.3390/app13042614 - 17 Feb 2023
Cited by 1 | Viewed by 1838
Abstract
Potato is one of the world’s four major food crops as an important resource cultivated in about 150 countries. As precision agriculture has recently attracted increasing attention for its role in improving productivity, interest in yield monitoring is also increasing. Yield monitoring is [...] Read more.
Potato is one of the world’s four major food crops as an important resource cultivated in about 150 countries. As precision agriculture has recently attracted increasing attention for its role in improving productivity, interest in yield monitoring is also increasing. Yield monitoring is a precision agriculture technology, and it can help farmhouse business management in the future by contributing to variable fertilization and supply and demand control. The present study was carried out to develop and evaluate a system that uses machine vision and deep learning technologies to estimate potato mass to monitor potato yield. The system performs object classification using the YOLOv5 algorithm to sort out potatoes among various foreign substances, object tracking using the DeepSORT algorithm to track the sorted potatoes, and volume calculation using the lengths of the major axis and minor axis of the tracked potatoes. The results of analyzing the function of the developed yield monitoring system showed an object detection rate of 95.2% and a weight measurement error of 9%, indicating that the computation load must be reduced through algorithm optimization to improve the accuracy and that error correction needs to be performed based on the potato position within the view angle. Full article
(This article belongs to the Special Issue AI，IoT and Remote Sensing in Precision Agriculture)
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