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Machine Learning and High-Throughput Phenotyping in Precision Agriculture

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 15 March 2025 | Viewed by 7129

Special Issue Editors


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Guest Editor
Department of Aerospace Engineering and Fluid Mechanics Agroforestry Engineering Area, University of Seville, Ctra. Sevilla-Utrera km.1, 41013 Seville, Spain
Interests: UAV imagery; ML for remote sensing; computer vision; crop protection strategies; AI-based weed mapping; satellite crop monitoring
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Terrestrial Information Systems Lab, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Interests: machine learning; multispectral hyperspectal image analysis; aquatic remote sensing; radiometric charactarization

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Guest Editor
1. ProcEDE, Faculty of Sciences and Techniques, Cadi Ayyad University, Marrakech, B.P 549, Av.Abdelkarim Elkhattabi, Guéliz Marrakech, Morocco
2. Center for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Ben Guerir, Morocco
Interests: monitoring crop water requirements; crop water stress detection; multispectral remote sensing for agricultural applications; agronomic modeling; data assimilation; retrieval of biophysical crop variables from a multisensor remote sensing approach
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Precision agriculture employs diverse technical methods to gather information about the crop growth environment, enabling precise and accurate agricultural micro-management of the entire production process. A pivotal facet of precision agriculture, crop phenotype research delves into the structural attributes of crop individuals or collectives, alongside their functional traits encompassing physical, physiological, and biochemical properties. Consequently, high-throughput phenotypic monitoring can accelerate the entire breeding process and provide important data support for formulating management strategy in precision agriculture.

The evolution of crop phenotype measurement technology encompasses stages such as manual measurement, two-dimensional photogrammetry, and three-dimensional measurement. The ability of remote sensing technology to non-destructively gather surface data through diverse electromagnetic spectrum bands is progressively assuming a more prominent role in precision agriculture. The rapid advancement of spectral and imaging technologies has introduced sophisticated sensors such as multi/hyperspectral, chlorophyll fluorescence, and lidar, offering efficient avenues for procuring crop phenotype data. Deploying a variety of sensors across distinct remote sensing platforms (spaceborne, airborne, and ground-based) facilitates swift acquisition of phenotypic data, enabling comprehensive multi-scale, multi-temporal monitoring of growth dynamics throughout the crop's developmental phase.

Moreover, machine learning has made breakthroughs in the field of remote sensing image processing. In applications such as object recognition and segmentation, image processing based on machine learning performs better than traditional methods. This Special Issue aims to combine machine learning technology and high-throughput phenotypic data to obtain the growth information of crops, indirectly predict the crop yield, monitor crop growth and biotic/abiotic stress responses, and thus realize agricultural precision, digitalization, informatization and intelligent management.

Dr. Jorge Martínez-Guanter
Dr. Akash Ashapure
Prof. Dr. Salah Er-Raki
Guest Editors

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Published Papers (5 papers)

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Research

23 pages, 7255 KiB  
Article
Exploring the Relationship Between Very-High-Resolution Satellite Imagery Data and Fruit Count for Predicting Mango Yield at Multiple Scales
by Benjamin Adjah Torgbor, Priyakant Sinha, Muhammad Moshiur Rahman, Andrew Robson, James Brinkhoff and Luz Angelica Suarez
Remote Sens. 2024, 16(22), 4170; https://doi.org/10.3390/rs16224170 - 8 Nov 2024
Viewed by 693
Abstract
Tree- and block-level prediction of mango yield is important for farm operations, but current manual methods are inefficient. Previous research has identified the accuracies of mango yield forecasting using very-high-resolution (VHR) satellite imagery and an ’18-tree’ stratified sampling method. However, this approach still [...] Read more.
Tree- and block-level prediction of mango yield is important for farm operations, but current manual methods are inefficient. Previous research has identified the accuracies of mango yield forecasting using very-high-resolution (VHR) satellite imagery and an ’18-tree’ stratified sampling method. However, this approach still requires infield sampling to calibrate canopy reflectance and the derived block-level algorithms are unable to translate to other orchards due to the influences of abiotic and biotic conditions. To better appreciate these influences, individual tree yields and corresponding canopy reflectance properties were collected from 2015 to 2021 for 1958 individual mango trees from 55 orchard blocks across 14 farms located in three mango growing regions of Australia. A linear regression analysis of the block-level data revealed the non-existence of a universal relationship between the 24 vegetation indices (VIs) derived from VHR satellite data and fruit count per tree, an outcome likely due to the influence of location, season, management and cultivar. The tree-level fruit count predicted using a random forest (RF) model trained on all calibration data produced a percentage root mean squared error (PRMSE) of 26.5% and a mean absolute error (MAE) of 48 fruits/tree. The lowest PRMSEs produced from RF-based models developed from location, season and cultivar subsets at the individual tree level ranged from 19.3% to 32.6%. At the block level, the PRMSE for the combined model was 10.1% and the lowest values for the location, seasonal and cultivar subset models varied between 7.2% and 10.0% upon validation. Generally, the block-level predictions outperformed the individual tree-level models. Maps were produced to provide mango growers with a visual representation of yield variability across orchards. This enables better identification and management of the influence of abiotic and biotic constraints on production. Future research could investigate the causes of spatial yield variability in mango orchards. Full article
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23 pages, 23664 KiB  
Article
Development of a UAS-Based Multi-Sensor Deep Learning Model for Predicting Napa Cabbage Fresh Weight and Determining Optimal Harvest Time
by Dong-Ho Lee and Jong-Hwa Park
Remote Sens. 2024, 16(18), 3455; https://doi.org/10.3390/rs16183455 - 18 Sep 2024
Viewed by 957
Abstract
The accurate and timely prediction of Napa cabbage fresh weight is essential for optimizing harvest timing, crop management, and supply chain logistics, which ultimately contributes to food security and price stabilization. Traditional manual sampling methods are labor-intensive and lack precision. This study introduces [...] Read more.
The accurate and timely prediction of Napa cabbage fresh weight is essential for optimizing harvest timing, crop management, and supply chain logistics, which ultimately contributes to food security and price stabilization. Traditional manual sampling methods are labor-intensive and lack precision. This study introduces an artificial intelligence (AI)-powered model that utilizes unmanned aerial systems (UAS)-based multi-sensor data to predict Napa cabbage fresh weight. The model was developed using high-resolution RGB, multispectral (MSP), and thermal infrared (TIR) imagery collected throughout the 2020 growing season. The imagery was used to extract various vegetation indices, crop features (vegetation fraction, crop height model), and a water stress indicator (CWSI). The deep neural network (DNN) model consistently outperformed support vector machine (SVM) and random forest (RF) models, achieving the highest accuracy (R2 = 0.82, RMSE = 0.47 kg) during the mid-to-late rosette growth stage (35–42 days after planting, DAP). The model’s accuracy improved with cabbage maturity, emphasizing the importance of the heading stage for fresh weight estimation. The model slightly underestimated the weight of Napa cabbages exceeding 5 kg, potentially due to limited samples and saturation effects of vegetation indices. The overall error rate was less than 5%, demonstrating the feasibility of this approach. Spatial analysis further revealed that the model accurately captured variability in Napa cabbage growth across different soil types and irrigation conditions, particularly reflecting the positive impact of drip irrigation. This study highlights the potential of UAS-based multi-sensor data and AI for accurate and non-invasive prediction of Napa cabbage fresh weight, providing a valuable tool for optimizing harvest timing and crop management. Future research should focus on refining the model for specific weight ranges and diverse environmental conditions, and extending its application to other crops. Full article
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19 pages, 9652 KiB  
Article
Focus on the Crop Not the Weed: Canola Identification for Precision Weed Management Using Deep Learning
by Michael Mckay, Monica F. Danilevicz, Michael B. Ashworth, Roberto Lujan Rocha, Shriprabha R. Upadhyaya, Mohammed Bennamoun and David Edwards
Remote Sens. 2024, 16(11), 2041; https://doi.org/10.3390/rs16112041 - 6 Jun 2024
Viewed by 1697
Abstract
Weeds pose a significant threat to agricultural production, leading to substantial yield losses and increased herbicide usage, with severe economic and environmental implications. This paper uses deep learning to explore a novel approach via targeted segmentation mapping of crop plants rather than weeds, [...] Read more.
Weeds pose a significant threat to agricultural production, leading to substantial yield losses and increased herbicide usage, with severe economic and environmental implications. This paper uses deep learning to explore a novel approach via targeted segmentation mapping of crop plants rather than weeds, focusing on canola (Brassica napus) as the target crop. Multiple deep learning architectures (ResNet-18, ResNet-34, and VGG-16) were trained for the pixel-wise segmentation of canola plants in the presence of other plant species, assuming all non-canola plants are weeds. Three distinct datasets (T1_miling, T2_miling, and YC) containing 3799 images of canola plants in varying field conditions alongside other plant species were collected with handheld devices at 1.5 m. The top performing model, ResNet-34, achieved an average precision of 0.84, a recall of 0.87, a Jaccard index (IoU) of 0.77, and a Macro F1 score of 0.85, with some variations between datasets. This approach offers increased feature variety for model learning, making it applicable to the identification of a wide range of weed species growing among canola plants, without the need for separate weed datasets. Furthermore, it highlights the importance of accounting for the growth stage and positioning of plants in field conditions when developing weed detection models. The study contributes to the growing field of precision agriculture and offers a promising alternative strategy for weed detection in diverse field environments, with implications for the development of innovative weed control techniques. Full article
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20 pages, 5205 KiB  
Article
Enhanced Leaf Area Index Estimation in Rice by Integrating UAV-Based Multi-Source Data
by Xiaoyue Du, Liyuan Zheng, Jiangpeng Zhu and Yong He
Remote Sens. 2024, 16(7), 1138; https://doi.org/10.3390/rs16071138 - 25 Mar 2024
Cited by 3 | Viewed by 1767
Abstract
The monitoring of crop growth, particularly the estimation of Leaf Area Index (LAI) using optical remote sensing techniques, has been a continuous area of research. However, it has become a challenge to accurately and rapidly interpret the spatial variation of LAI under nitrogen [...] Read more.
The monitoring of crop growth, particularly the estimation of Leaf Area Index (LAI) using optical remote sensing techniques, has been a continuous area of research. However, it has become a challenge to accurately and rapidly interpret the spatial variation of LAI under nitrogen stress. To tackle these issues, this study aimed to explore the potential for precise LAI estimation by integrating multiple features, such as average spectral reflectance (ASR), vegetation index, and textures, obtained through an unmanned aerial vehicle (UAV). The study employed the partial least squares method (PLS), extreme learning machine (ELM), random forest (RF), and support vector machine (SVM) to build the LAI estimation model under nitrogen stress. The findings of this study revealed the following: (i) texture features generally exhibited greater sensitivity to LAI compared to ASR and VIs. (ii) Utilizing a multi-source feature fusion strategy enhanced the model’s accuracy in predicting LAI compared to using a single feature. The best RP2 and RMSEP of the estimated LAI were 0.78 and 0.49, respectively, achieved by RF through the combination of ASR, VIs, and textures. (iii) Among the four machine learning algorithms, RF and SVM displayed strong potential in estimating LAI of rice crops under nitrogen stress. The RP2 of the estimated LAI using ASR + VIs + texture, in descending order, were 0.78, 0.73, 0.67, and 0.62, attained by RF, SVM, PLS, and ELM, respectively. This study analyzed the spatial variation of LAI in rice using remote sensing techniques, providing a crucial theoretical foundation for crop management in the field. Full article
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31 pages, 30389 KiB  
Article
Preharvest Durum Wheat Yield, Protein Content, and Protein Yield Estimation Using Unmanned Aerial Vehicle Imagery and Pléiades Satellite Data in Field Breeding Experiments
by Dessislava Ganeva, Eugenia Roumenina, Petar Dimitrov, Alexander Gikov, Violeta Bozhanova, Rangel Dragov, Georgi Jelev and Krasimira Taneva
Remote Sens. 2024, 16(3), 559; https://doi.org/10.3390/rs16030559 - 31 Jan 2024
Cited by 1 | Viewed by 1302
Abstract
Unmanned aerial vehicles (UAVs) are extensively used to gather remote sensing data, offering high image resolution and swift data acquisition despite being labor-intensive. In contrast, satellite-based remote sensing, providing sub-meter spatial resolution and frequent revisit times, could serve as an alternative data source [...] Read more.
Unmanned aerial vehicles (UAVs) are extensively used to gather remote sensing data, offering high image resolution and swift data acquisition despite being labor-intensive. In contrast, satellite-based remote sensing, providing sub-meter spatial resolution and frequent revisit times, could serve as an alternative data source for phenotyping. In this study, we separately evaluated pan-sharpened Pléiades satellite imagery (50 cm) and UAV imagery (2.5 cm) to phenotype durum wheat in small-plot (12 m × 1.10 m) breeding trials. The Gaussian process regression (GPR) algorithm, which provides predictions with uncertainty estimates, was trained with spectral bands and а selected set of vegetation indexes (VIs) as independent variables. Grain protein content (GPC) was better predicted with Pléiades data at the growth stage of 20% of inflorescence emerged but with only moderate accuracy (validation R2: 0.58). The grain yield (GY) and protein yield (PY) were better predicted using UAV data at the late milk and watery ripe growth stages, respectively (validation: R2 0.67 and 0.62, respectively). The cumulative VIs (the sum of VIs over the available images within the growing season) did not increase the accuracy of the models for either sensor. When mapping the estimated parameters, the spatial resolution of Pléiades revealed certain limitations. Nevertheless, our findings regarding GPC suggested that the usefulness of pan-sharpened Pléiades images for phenotyping should not be dismissed and warrants further exploration, particularly for breeding experiments with larger plot sizes. Full article
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