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Advances in Sensor Systems and Data Analysis for Crop Phenotyping

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 7563

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Special Issue Editors

Dr. Lingfeng Duan

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Guest Editor

1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
2. Key Laboratory of Agricultural Equipment for the Middle and Lower Reaches of the Yangtze River, Ministry of Agriculture, Wuhan 430070, China
Interests: plant phenotyping; computer vision; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Hui Feng

E-Mail Website
Guest Editor

1. College of Informations, Huazhong Agricultural University, Wuhan 430070, China
2. Hubei Engineering Technology Research Center of Agricultural Big Data, Wuhan 430070, China
Interests: crop phenotyping; computer vision; data mining; hyperspectral

Special Issue Information

Dear Colleagues,

Crop phenotyping is gaining more and more attention because it enables noninvasive quantification of plant structure, function, and interactions with environments. In recent years, sensors, machine vision, and automation technology have been widely adopted for large-scale phenotyping data acquisition and processing to augment automation and promote efficiency. Advances in a range of technologies, from sensors to data analysis, combined with system integration and decreasing costs, mean that crop morphology and physiology can be measured non-destructively and repeatedly across populations and throughout the whole growth period.

This Special Issue invites submissions addressing sensor systems and data analysis in crop phenotyping. The scope of this Special Issue covers the latest technologies in crop phenotyping for data acquisition, data management, data interpretation, and modeling. Specific topics of interest include, but are not limited to, the following:

Advanced sensors for crop phenotyping;
Statistics, bioinformatics, machine learning, and deep learning in crop phenotyping;
Data analysis tools and software for crop phenotyping;
Data management methodologies and tools for crop phenotyping;
Plant growth modeling, simulation, visualization, and application;

Applications of crop phenotyping in genomics, genetics, physiology, molecular biology, crop cultivation, crop breeding, and other plant-related domains.

Dr. Lingfeng Duan
Dr. Hui Feng
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

crop phenotyping
sensing
data analysis
machine learning
deep learning
data management
plant growth modeling

Published Papers (4 papers)

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Research

14 pages, 2707 KiB

Open AccessArticle

Identifying the Growth Status of Hydroponic Lettuce Based on YOLO-EfficientNet

by Yidong Wang, Mingge Wu and Yunde Shen

Plants 2024, 13(3), 372; https://doi.org/10.3390/plants13030372 - 26 Jan 2024

Cited by 1 | Viewed by 1540

Abstract

Hydroponic lettuce was prone to pest and disease problems after transplantation. Manual identification of the current growth status of each hydroponic lettuce not only consumed time and was prone to errors but also failed to meet the requirements of high-quality and efficient lettuce cultivation. In response to this issue, this paper proposed a method called YOLO-EfficientNet for identifying the growth status of hydroponic lettuce. Firstly, the video data of hydroponic lettuce were processed to obtain individual frame images. And 2240 images were selected from these frames as the image dataset A. Secondly, the YOLO-v8n object detection model was trained using image dataset A to detect the position of each hydroponic lettuce in the video data. After selecting the targets based on the predicted bounding boxes, 12,000 individual lettuce images were obtained by cropping, which served as image dataset B. Finally, the EfficientNet-v2s object classification model was trained using image dataset B to identify three growth statuses (Healthy, Diseases, and Pests) of hydroponic lettuce. The results showed that, after training image dataset A using the YOLO-v8n model, the accuracy and recall were consistently around 99%. After training image dataset B using the EfficientNet-v2s model, it achieved excellent scores of 95.78 for Val-acc, 94.68 for Test-acc, 96.02 for Recall, 96.32 for Precision, and 96.18 for F1-score. Thus, the method proposed in this paper had potential in the agricultural application of identifying and classifying the growth status in hydroponic lettuce. Full article

(This article belongs to the Special Issue Advances in Sensor Systems and Data Analysis for Crop Phenotyping)

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20 pages, 3643 KiB

Open AccessArticle

Assessment of Soybean Lodging Using UAV Imagery and Machine Learning

by Shagor Sarkar, Jing Zhou, Andrew Scaboo, Jianfeng Zhou, Noel Aloysius and Teng Teeh Lim

Plants 2023, 12(16), 2893; https://doi.org/10.3390/plants12162893 - 8 Aug 2023

Cited by 5 | Viewed by 1606

Abstract

Plant lodging is one of the most essential phenotypes for soybean breeding programs. Soybean lodging is conventionally evaluated visually by breeders, which is time-consuming and subject to human errors. This study aimed to investigate the potential of unmanned aerial vehicle (UAV)-based imagery and machine learning in assessing the lodging conditions of soybean breeding lines. A UAV imaging system equipped with an RGB (red-green-blue) camera was used to collect the imagery data of 1266 four-row plots in a soybean breeding field at the reproductive stage. Soybean lodging scores were visually assessed by experienced breeders, and the scores were grouped into four classes, i.e., non-lodging, moderate lodging, high lodging, and severe lodging. UAV images were stitched to build orthomosaics, and soybean plots were segmented using a grid method. Twelve image features were extracted from the collected images to assess the lodging scores of each breeding line. Four models, i.e., extreme gradient boosting (XGBoost), random forest (RF), K-nearest neighbor (KNN) and artificial neural network (ANN), were evaluated to classify soybean lodging classes. Five data preprocessing methods were used to treat the imbalanced dataset to improve classification accuracy. Results indicate that the preprocessing method SMOTE-ENN consistently performs well for all four (XGBoost, RF, KNN, and ANN) classifiers, achieving the highest overall accuracy (OA), lowest misclassification, higher F1-score, and higher Kappa coefficient. This suggests that Synthetic Minority Oversampling-Edited Nearest Neighbor (SMOTE-ENN) may be a good preprocessing method for using unbalanced datasets and the classification task. Furthermore, an overall accuracy of 96% was obtained using the SMOTE-ENN dataset and ANN classifier. The study indicated that an imagery-based classification model could be implemented in a breeding program to differentiate soybean lodging phenotype and classify lodging scores effectively. Full article

(This article belongs to the Special Issue Advances in Sensor Systems and Data Analysis for Crop Phenotyping)

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20 pages, 4641 KiB

Open AccessArticle

Machine Learning-Based Crop Stress Detection in Greenhouses

by Angeliki Elvanidi and Nikolaos Katsoulas

Plants 2023, 12(1), 52; https://doi.org/10.3390/plants12010052 - 22 Dec 2022

Cited by 5 | Viewed by 2405

Abstract

Greenhouse climate control systems are usually based on greenhouse microclimate settings to exert any control. However, to save energy, water and nutrients, additional parameters related to crop performance and physiology will have to be considered. In addition, detecting crop stress before it is clearly visible by naked eye is an advantage that could aid in microclimate control. In this study, a Machine Learning (ML) model which takes into account microclimate and crop physiological data to detect different types of crop stress was developed and tested. For this purpose, a multi-sensor platform was used to record tomato plant physiological characteristics under different fertigation and air temperature conditions. The innovation of the current model lies in the integration of photosynthesis rate (Ps) values estimated by means of remote sensing using a photochemical reflectance index (PRI). Through this process, the time-series Ps data were combined with crop leaf temperature and microclimate data by means of the ML model. Two different algorithms were evaluated: Gradient Boosting (GB) and MultiLayer perceptron (MLP). Two runs with different structures took place for each algorithm. In RUN 1, there were more feature inputs than the outputs to build a model with high predictive accuracy. However, in order to simplify the process and develop a user-friendly approach, a second, different run was carried out. Thus, in RUN 2, the inputs were fewer than the outputs, and that is why the performance of the model in this case was lower than in the case of RUN 1. Particularly, MLP showed 91% and 83% accuracy in the training sample, and 89% and 82% in testing sample, for RUNs 1 and 2, respectively. GB showed 100% accuracy in the training sample for both runs, and 91% and 83% in testing sample in RUN 1 and RUN 2, respectively. To improve the accuracy of RUN 2, a larger database is required. Both models, however, could easily be incorporated into existing greenhouse climate monitoring and control systems, replacing human experience in detecting greenhouse crop stress conditions. Full article

(This article belongs to the Special Issue Advances in Sensor Systems and Data Analysis for Crop Phenotyping)

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20 pages, 917 KiB

Open AccessArticle

Modeling of Flowering Time in Vigna radiata with Artificial Image Objects, Convolutional Neural Network and Random Forest

by Maria Bavykina, Nadezhda Kostina, Cheng-Ruei Lee, Roland Schafleitner, Eric Bishop-von Wettberg, Sergey V. Nuzhdin, Maria Samsonova, Vitaly Gursky and Konstantin Kozlov

Plants 2022, 11(23), 3327; https://doi.org/10.3390/plants11233327 - 1 Dec 2022

Viewed by 1436

Abstract

Flowering time is an important target for breeders in developing new varieties adapted to changing conditions. In this work, a new approach is proposed in which the SNP markers influencing time to flowering in mung bean are selected as important features in a random forest model. The genotypic and weather data are encoded in artificial image objects, and a model for flowering time prediction is constructed as a convolutional neural network. The model uses weather data for only a limited time period of 5 days before and 20 days after planting and is capable of predicting the time to flowering with high accuracy. The most important factors for model solution were identified using saliency maps and a Score-CAM method. Our approach can help breeding programs harness genotypic and phenotypic diversity to more effectively produce varieties with a desired flowering time. Full article

(This article belongs to the Special Issue Advances in Sensor Systems and Data Analysis for Crop Phenotyping)

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Advances in Sensor Systems and Data Analysis for Crop Phenotyping

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