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Multimodal Remote Sensing and Imaging for Precision Agriculture

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 9538

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Special Issue Editor

Prof. Dr. Benoit Mercatoris

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Guest Editor

TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, Liège University, Passage des Déportés, 2, 5030 Gembloux, Belgium
Interests: machine vision-based crop phenotyping; sensing technology for precision agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agriculture systems are facing a variety of stresses (e.g., diseases and insect pests, drought, heat, cold, frost, flooding, excess or deficiency of fertilization, and environmental pollution) due to ever-increasing human interference and ongoing climate change. It is essential to accurately and rapidly identify and quantify these stresses to support decision making. The rapid development of multimodal imaging techniques has greatly facilitated classification, monitoring, identification, diagnosis, and assessment in agriculture. Nevertheless, there are still many urgent and critical issues that need to be addressed, such as small-sample classification, spectral dimensionality reduction, data fusion, sensitive spectral band selection, multiple stress identification, growth condition monitoring, early disaster warning, etc. This Special Issue is aimed at a global research community involved in sensor development, data acquisition and data treatment for precision agriculture. Specific topics include but are not limited to the following:

Crop mapping;
Vegetation health monitoring;
Species detection (e.g., illicit/invasive plants);
Agricultural crop assessment;
Yield prediction and quality;
In-field phenotyping estimation;
Plant disease detection;
Model-based trait analysis (e.g., by considering 3D plant models);
Crop mapping based on multimodal acquisitions (e.g., multi/hyperspectral, thermal, LiDAR point clouds, fluorescence, and SAR imaging);
Time-series analysis for agriculture monitoring;
In-situ remote sensing measurements (e.g., robotic vision).

Prof. Dr. Benoit Mercatoris
Guest Editor

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Keywords

hyperspectral imaging
multispectral imaging
thermal imaging
in-field crop phenotyping
crop growth monitoring
nutrient diagnosis
stress detection
geophysical parameters
data fusion
dimensionality reduction
feature extraction
sensitive features

Published Papers (5 papers)

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Research

20 pages, 21044 KiB

Open AccessArticle

Sooty Mold Detection on Citrus Tree Canopy Using Deep Learning Algorithms

by Bryan Vivas Apacionado and Tofael Ahamed

Sensors 2023, 23(20), 8519; https://doi.org/10.3390/s23208519 - 17 Oct 2023

Viewed by 1233

Abstract

Sooty mold is a common disease found in citrus plants and is characterized by black fungi growth on fruits, leaves, and branches. This mold reduces the plant’s ability to carry out photosynthesis. In small leaves, it is very difficult to detect sooty mold at the early stages. Deep learning-based image recognition techniques have the potential to identify and diagnose pest damage and diseases such as sooty mold. Recent studies used advanced and expensive hyperspectral or multispectral cameras attached to UAVs to examine the canopy of the plants and mid-range cameras to capture close-up infected leaf images. To bridge the gap on capturing canopy level images using affordable camera sensors, this study used a low-cost home surveillance camera to monitor and detect sooty mold infection on citrus canopy combined with deep learning algorithms. To overcome the challenges posed by varying light conditions, the main reason for using specialized cameras, images were collected at night, utilizing the camera’s built-in night vision feature. A total of 4200 sliced night-captured images were used for training, 200 for validation, and 100 for testing, employed on the YOLOv5m, YOLOv7, and CenterNet models for comparison. The results showed that YOLOv7 was the most accurate in detecting sooty molds at night, with 74.4% mAP compared to YOLOv5m (72%) and CenterNet (70.3%). The models were also tested using preprocessed (unsliced) night images and day-captured sliced and unsliced images. The testing on preprocessed (unsliced) night images demonstrated the same trend as the training results, with YOLOv7 performing best compared to YOLOv5m and CenterNet. In contrast, testing on the day-captured images had underwhelming outcomes for both sliced and unsliced images. In general, YOLOv7 performed best in detecting sooty mold infections at night on citrus canopy and showed promising potential in real-time orchard disease monitoring and detection. Moreover, this study demonstrated that utilizing a cost-effective surveillance camera and deep learning algorithms can accurately detect sooty molds at night, enabling growers to effectively monitor and identify occurrences of the disease at the canopy level. Full article

(This article belongs to the Special Issue Multimodal Remote Sensing and Imaging for Precision Agriculture)

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27 pages, 34079 KiB

Open AccessArticle

Detection of Defective Lettuce Seedlings Grown in an Indoor Environment under Different Lighting Conditions Using Deep Learning Algorithms

by Munirah Hayati Hamidon and Tofael Ahamed

Sensors 2023, 23(13), 5790; https://doi.org/10.3390/s23135790 - 21 Jun 2023

Cited by 1 | Viewed by 1724

Abstract

Sorting seedlings is laborious and requires attention to identify damage. Separating healthy seedlings from damaged or defective seedlings is a critical task in indoor farming systems. However, sorting seedlings manually can be challenging and time-consuming, particularly under complex lighting conditions. Different indoor lighting conditions can affect the visual appearance of the seedlings, making it difficult for human operators to accurately identify and sort the seedlings consistently. Therefore, the objective of this study was to develop a defective-lettuce-seedling-detection system under different indoor cultivation lighting systems using deep learning algorithms to automate the seedling sorting process. The seedling images were captured under different indoor lighting conditions, including white, blue, and red. The detection approach utilized and compared several deep learning algorithms, specifically CenterNet, YOLOv5, YOLOv7, and faster R-CNN to detect defective seedlings in indoor farming environments. The results demonstrated that the mean average precision (mAP) of YOLOv7 (97.2%) was the highest and could accurately detect defective lettuce seedlings compared to CenterNet (82.8%), YOLOv5 (96.5%), and faster R-CNN (88.6%). In terms of detection under different light variables, YOLOv7 also showed the highest detection rate under white and red/blue/white lighting. Overall, the detection of defective lettuce seedlings by YOLOv7 shows great potential for introducing automated seedling-sorting systems and classification under actual indoor farming conditions. Defective-seedling-detection can improve the efficiency of seedling-management operations in indoor farming. Full article

(This article belongs to the Special Issue Multimodal Remote Sensing and Imaging for Precision Agriculture)

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20 pages, 8591 KiB

Open AccessArticle

Can Satellites Predict Yield? Ensemble Machine Learning and Statistical Analysis of Sentinel-2 Imagery for Processing Tomato Yield Prediction

by Nicoleta Darra, Borja Espejo-Garcia, Aikaterini Kasimati, Olga Kriezi, Emmanouil Psomiadis and Spyros Fountas

Sensors 2023, 23(5), 2586; https://doi.org/10.3390/s23052586 - 26 Feb 2023

Cited by 5 | Viewed by 1974

Abstract

In this paper, we propose an innovative approach for robust prediction of processing tomato yield using open-source AutoML techniques and statistical analysis. Sentinel-2 satellite imagery was deployed to obtain values of five (5) selected vegetation indices (VIs) during the growing season of 2021 (April to September) at 5-day intervals. Actual recorded yields were collected across 108 fields, corresponding to a total area of 410.10 ha of processing tomato in central Greece, to assess the performance of Vis at different temporal scales. In addition, VIs were connected with the crop phenology to establish the annual dynamics of the crop. The highest Pearson coefficient (r) values occurred during a period of 80 to 90 days, indicating the strong relationship between the VIs and the yield. Specifically, RVI presented the highest correlation values of the growing season at 80 (r = 0.72) and 90 days (r = 0.75), while NDVI performed better at 85 days (r = 0.72). This output was confirmed by the AutoML technique, which also indicated the highest performance of the VIs during the same period, with the values of the adjusted R² ranging from 0.60 to 0.72. The most precise results were obtained with the combination of ARD regression and SVR, which was the most successful combination for building an ensemble (adj. R² = 0.67 ± 0.02). Full article

(This article belongs to the Special Issue Multimodal Remote Sensing and Imaging for Precision Agriculture)

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18 pages, 6015 KiB

Open AccessArticle

Wide-Field-of-View Multispectral Camera Design for Continuous Turfgrass Monitoring

by Lien Smeesters, Jef Verbaenen, Luca Schifano, Michael Vervaeke, Hugo Thienpont, Giancarlo Teti, Alessio Forconi and Filippo Lulli

Sensors 2023, 23(5), 2470; https://doi.org/10.3390/s23052470 - 23 Feb 2023

Viewed by 2222

Abstract

Sustainably using resources, while reducing the use of chemicals, is of major importance in agriculture, including turfgrass monitoring. Today, crop monitoring often uses camera-based drone sensing, offering an accurate evaluation but typically requiring a technical operator. To enable autonomous and continuous monitoring, we propose a novel five-channel multispectral camera design suitable for integrating it inside lighting fixtures and enabling the sensing of a multitude of vegetation indices by covering visible, near-infrared and thermal wavelength bands. To limit the number of cameras, and in contrast to the drone-sensing systems that show a small field of view, a novel wide-field-of-view imaging design is proposed, featuring a field of view exceeding 164°. This paper presents the development of the five-channel wide-field-of-view imaging design, starting from the optimization of the design parameters and moving toward a demonstrator setup and optical characterization. All imaging channels show an excellent image quality, indicated by an MTF exceeding 0.5 at a spatial frequency of 72 lp/mm for the visible and near-infrared imaging designs and 27 lp/mm for the thermal channel. Consequently, we believe our novel five-channel imaging design paves the way toward autonomous crop monitoring while optimizing resource usage. Full article

(This article belongs to the Special Issue Multimodal Remote Sensing and Imaging for Precision Agriculture)

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25 pages, 5042 KiB

Open AccessArticle

Application of Near Infrared Spectroscopy to Monitor the Quality Change of Sour Cherry Stored under Modified Atmosphere Conditions

by Gergo Szabo, Flora Vitalis, Zsuzsanna Horvath-Mezofi, Monika Gob, Juan Pablo Aguinaga Bosquez, Zoltan Gillay, Tamás Zsom, Lien Le Phuong Nguyen, Geza Hitka, Zoltan Kovacs and Laszlo Friedrich

Sensors 2023, 23(1), 479; https://doi.org/10.3390/s23010479 - 02 Jan 2023

Viewed by 1665

Abstract

Determining and applying ‘good’ postharvest and quality control practices for otherwise highly sensitive fruits, such as sour cherry, is critical, as they serve as excellent media for a wide variety of microbial contaminants. The objective of this research was to report two series of experiments on the modified atmosphere storage (MAP) of sour cherries (Prunus cerasus L. var. Kántorjánosi, Újfehértói fürtös). Firstly, the significant effect of different washing pre-treatments on various quality indices was examined (i.e., headspace gas composition, weight loss, decay rate, color, firmness, soluble solid content, total plate count) in MAP-packed fruits. Subsequently, the applicability of near infrared (NIR) spectroscopy combined with chemometrics was investigated to detect the effect of various storage conditions (packed as control or MAP, stored at 3 or 5 °C) on sour cherries of different perceived ripeness. Significant differences were found for oxygen concentration when two perforations were applied on the packages of ‘Kántorjánosi’ (p < 0.01); weight loss when ‘Kánorjánosi’ (p < 0.001) and ‘Újfehértói fürtös’ (p < 0.01) were packed in MAP; SSC when ‘Újfehértói fürtös’ samples were ozone-treated (p < 0.05); and total plate count when ‘Kántorjánosi’ samples were ozone-treated (p < 0.01). The difference spectra reflected the high variability in the samples, and the detectable effects of different packaging. Based on the investigations with the soft independent modelling of class analogies (SIMCA), different packaging and storage resulted in significant differences in most of the cases even on the first storage day, which in many cases increased by the end of storage. The soft independent modelling of class analogies proved to be suitable for classification with apparent error rates between 0 and 0.5 during prediction regardless of ripeness. The research findings suggest the further correlation of NIR spectroscopic and reference parameters to support postharvest handling and fast quality control. Full article

(This article belongs to the Special Issue Multimodal Remote Sensing and Imaging for Precision Agriculture)

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