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High-Throughput Phenotyping in Plants Using Remote Sensing

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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 15640

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Special Issue Editors

Dr. Paulo Eduardo Teodoro

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Department of Agronomy, Universidade Federal de Mato Grosso do Sul, Chapadão do Sul 7956-000, Brazil
Interests: statistics; multivariate analysis; plant breending; biometrics; remote sensing; sensors; genomic selection; geostatistics; precision agriculture
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Dr. Carlos Antonio Da Silva Junior

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Department of Geography, State University of Mato Grosso (UNEMAT), Sinop 78550-000, MT, Brazil
Interests: remote sensing; LULCC; environment; spatial analysis; climate change
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Dr. Larissa Pereira Ribeiro Teodoro

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Department of Agronomy, Universidade Federal de Mato Grosso do Sul, Chapadão do Sul 7956-000, Brazil
Interests: UAV; random forest; nitrogen; maize
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Special Issue Information

Dear Colleagues,

Ensuring that food production is sufficient to meet the needs of a human population that is expected to grow to more than 9 billion by 2050 is a major challenge for plant science. Thus, the integration between remote sensing and plant breeding for high-throughput phenotyping can contribute to an increase in agricultural production. The integrated use of remote sensing and computational intelligence makes it possible to make predictions about the characteristics of plants and their agronomic performance, especially size, cycle and productivity, which are crucial for the success of the production system. The measurement of these characteristics in the field, when carried out in a conventional way, demands a lot of time and manpower, especially when considering the simultaneous evaluation of several cultures. Using phenotyping techniques that combine remote sensing and computational intelligence, greater efficiency is obtained in cultivar evaluation trials, such as labor savings, speed in the evaluation of agronomic characteristics and greater reliability of the information obtained. Therefore, the main topics for manuscripts of this SI are high-precision phenotyping using UAV-multispectral or hyperspectral sensors, sensors embedded in satellites, and on-site measurements for:

Agronomic traits of plants such as: height, cycle, grain yield, and others;
Physiological traits of plants such as: photosynthesis, transpiration, water use efficiency;
Nutritional traits of plants such as: foliar and grain macro and micronutrient contents;
Industrial traits of plants such as: levels of protein, oil, and others;
Approaches that use computational intelligence to associate these traces with the spectral variables collected are also encouraged.

Dr. Paulo Eduardo Teodoro
Dr. Carlos Antonio Da Silva Junior
Dr. Larissa Pereira Ribeiro Teodoro
Guest Editors

Manuscript Submission Information

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Keywords

computational intelligence
agronomic traits of plants
physiological traits of plants
nutritional traits of plants
industrial traits of plants
UAV-multispectral sensor

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Published Papers (5 papers)

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17 pages, 2891 KiB

Open AccessArticle

Estimation of Winter Wheat Plant Nitrogen Concentration from UAV Hyperspectral Remote Sensing Combined with Machine Learning Methods

by Xiaokai Chen, Fenling Li, Botai Shi and Qingrui Chang

Remote Sens. 2023, 15(11), 2831; https://doi.org/10.3390/rs15112831 - 29 May 2023

Cited by 11 | Viewed by 2334

Abstract

Nitrogen is one of the most important macronutrients and plays an essential role in the growth and development of winter wheat. It is very crucial to diagnose the nitrogen status timely and accurately for applying a precision nitrogen management (PNM) strategy to the guidance of nitrogen fertilizer in the field. The main purpose of this study was to use three different prediction methods to evaluate winter wheat plant nitrogen concentration (PNC) at booting, heading, flowering, filling, and the whole growth stage in the Guanzhong area from unmanned aerial vehicle (UAV) hyperspectral imagery. These methods include (1) the parametric regression method; (2) linear nonparametric regression methods (stepwise multiple linear regression (SMLR) and partial least squares regression (PLSR)); and (3) machine learning methods (random forest regression (RFR), support vector machine regression (SVMR), and extreme learning machine regression (ELMR)). The purpose of this study was also to pay attention to the impact of different growth stages on the accuracy of the model. The results showed that compared with parametric regression and linear nonparametric regression, the machine learning regression method could evidently improve the estimation accuracy of winter wheat PNC, especially using SVMR and RFR, the training set of the model at flowering and filling stage explained 93% and 92% of the PNC variability respectively. The testing set of the model at flowering and filling stages explained 88% and 91% of the PNC variability, the root mean square error of the validation set (RMSE_testing) was 0.82 and 1.23, and the relative prediction deviation (RPD) was 2.58 and 2.40, respectively. Therefore, a conclusion was drawn that it was the best choice to estimate winter wheat PNC at the flowering and filling stage from UAV hyperspectral imagery. Using machine learning methods, SVMR and RFR, respectively, could achieve the most outstanding estimation performance, which could provide a theoretical basis for putting forward the PNM strategy. Full article

(This article belongs to the Special Issue High-Throughput Phenotyping in Plants Using Remote Sensing)

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27 pages, 5659 KiB

Open AccessArticle

Predicting Dry Pea Maturity Using Machine Learning and Advanced Sensor Fusion with Unmanned Aerial Systems (UASs)

by Aliasghar Bazrafkan, Harry Navasca, Jeong-Hwa Kim, Mario Morales, Josephine Princy Johnson, Nadia Delavarpour, Nadeem Fareed, Nonoy Bandillo and Paulo Flores

Remote Sens. 2023, 15(11), 2758; https://doi.org/10.3390/rs15112758 - 25 May 2023

Cited by 6 | Viewed by 3114

Abstract

Maturity is an important trait in dry pea breeding programs, but the conventional process predominately used to measure this trait can be time-consuming, labor-intensive, and prone to errors. Therefore, a more efficient and accurate approach would be desirable to support dry pea breeding programs. This study presents a novel approach for measuring dry pea maturity using machine learning algorithms and unmanned aerial systems (UASs)-collected data. We evaluated the abilities of five machine learning algorithms (random forest, artificial neural network, support vector machine, K-nearest neighbor, and naïve Bayes) to accurately predict dry pea maturity on field plots. The machine learning algorithms considered a range of variables, including crop height metrics, narrow spectral bands, and 18 distinct color and spectral vegetation indices. Backward feature elimination was used to select the most important features by iteratively removing insignificant ones until the model’s predictive performance was optimized. The study’s findings reveal that the most effective approach for assessing dry pea maturity involved a combination of narrow spectral bands, red-edge, near-infrared (NIR), and RGB-based vegetation indices, along with image textural metrics and crop height metrics. The implementation of a random forest model further enhanced the accuracy of the results, exhibiting the highest level of accuracy with a 0.99 value for all three metrics precision, recall, and f1 scores. The sensitivity analysis revealed that spectral features outperformed structural features when predicting pea maturity. While multispectral cameras achieved the highest accuracy, the use of RGB cameras may still result in relatively high accuracy, making them a practical option for use in scenarios where cost is a limiting factor. In summary, this study demonstrated the effectiveness of coupling machine learning algorithms, UASs-borne LIDAR, and multispectral data to accurately assess maturity in peas. Full article

(This article belongs to the Special Issue High-Throughput Phenotyping in Plants Using Remote Sensing)

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18 pages, 38240 KiB

Open AccessArticle

Detection and Counting of Corn Plants in the Presence of Weeds with Convolutional Neural Networks

by Canek Mota-Delfin, Gilberto de Jesús López-Canteñs, Irineo Lorenzo López-Cruz, Eugenio Romantchik-Kriuchkova and Juan Carlos Olguín-Rojas

Remote Sens. 2022, 14(19), 4892; https://doi.org/10.3390/rs14194892 - 30 Sep 2022

Cited by 19 | Viewed by 3096

Abstract

Corn is an important part of the Mexican diet. The crop requires constant monitoring to ensure production. For this, plant density is often used as an indicator of crop yield, since knowing the number of plants helps growers to manage and control their plots. In this context, it is necessary to detect and count corn plants. Therefore, a database of aerial RGB images of a corn crop in weedy conditions was created to implement and evaluate deep learning algorithms. Ten flight missions were conducted, six with a ground sampling distance (GSD) of 0.33 cm/pixel at vegetative stages from V3 to V7 and four with a GSD of 1.00 cm/pixel for vegetative stages V6, V7 and V8. The detectors compared were YOLOv4, YOLOv4-tiny, YOLOv4-tiny-3l, and YOLOv5 versions s, m and l. Each detector was evaluated at intersection over union (IoU) thresholds of 0.25, 0.50 and 0.75 at confidence intervals of 0.05. A strong F1-Score penalty was observed at the IoU threshold of 0.75 and there was a 4.92% increase in all models for an IoU threshold of 0.25 compared to 0.50. For confidence levels above 0.35, YOLOv4 shows greater robustness in detection compared to the other models. Considering the mode of 0.3 for the confidence level that maximizes the F1-Score metric and the IoU threshold of 0.25 in all models, YOLOv5-s obtained a mAP of 73.1% with a coefficient of determination (

R^{2}

) of 0.78 and a relative mean square error (rRMSE) of 42% in the plant count, followed by YOLOv4 with a mAP of 72.0%,

R^{2}

of 0.81 and rRMSE of 39.5%. Full article

(This article belongs to the Special Issue High-Throughput Phenotyping in Plants Using Remote Sensing)

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13 pages, 2916 KiB

Open AccessTechnical Note

Machine Learning in the Classification of Soybean Genotypes for Primary Macronutrients’ Content Using UAV–Multispectral Sensor

by Dthenifer Cordeiro Santana, Marcelo Carvalho Minhoto Teixeira Filho, Marcelo Rinaldi da Silva, Paulo Henrique Menezes das Chagas, João Lucas Gouveia de Oliveira, Fábio Henrique Rojo Baio, Cid Naudi Silva Campos, Larissa Pereira Ribeiro Teodoro, Carlos Antonio da Silva Junior, Paulo Eduardo Teodoro and Luciano Shozo Shiratsuchi

Remote Sens. 2023, 15(5), 1457; https://doi.org/10.3390/rs15051457 - 5 Mar 2023

Cited by 11 | Viewed by 2860

Abstract

Using spectral data to quantify nitrogen (N), phosphorus (P), and potassium (K) contents in soybean plants can help breeding programs develop fertilizer-efficient genotypes. Employing machine learning (ML) techniques to classify these genotypes according to their nutritional content makes the analyses performed in the programs even faster and more reliable. Thus, the objective of this study was to find the best ML algorithm(s) and input configurations in the classification of soybean genotypes for higher N, P, and K leaf contents. A total of 103 F₂ soybean populations were evaluated in a randomized block design with two repetitions. At 60 days after emergence (DAE), spectral images were collected using a Sensefly eBee RTK fixed-wing remotely piloted aircraft (RPA) with autonomous take-off, flight plan, and landing control. The eBee was equipped with the Parrot Sequoia multispectral sensor. Reflectance values were obtained in the following spectral bands (SBs): red (660 nm), green (550 nm), NIR (735 nm), and red-edge (790 nm), which were used to calculate the vegetation index (VIs): normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), green normalized difference vegetation index (GNDVI), soil-adjusted vegetation index (SAVI), modified soil-adjusted vegetation index (MSAVI), modified chlorophyll absorption in reflectance index (MCARI), enhanced vegetation index (EVI), and simplified canopy chlorophyll content index (SCCCI). At the same time of the flight, leaves were collected in each experimental unit to obtain the leaf contents of N, P, and K. The data were submitted to a Pearson correlation analysis. Subsequently, a principal component analysis was performed together with the k-means algorithm to define two clusters: one whose genotypes have high leaf contents and another whose genotypes have low leaf contents. Boxplots were generated for each cluster according to the content of each nutrient within the groups formed, seeking to identify which set of genotypes has higher nutrient contents. Afterward, the data were submitted to machine learning analysis using the following algorithms: decision tree algorithms J48 and REPTree, random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR, used as control). The clusters were used as output variables of the classification models used. The spectral data were used as input variables for the models, and three different configurations were tested: using SB only, using VIs only, and using SBs+VIs. The J48 and SVM algorithms had the best performance in classifying soybean genotypes. The best input configuration for the algorithms was using the spectral bands as input. Full article

(This article belongs to the Special Issue High-Throughput Phenotyping in Plants Using Remote Sensing)

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13 pages, 2171 KiB

Open AccessTechnical Note

Maize Yield Prediction with Machine Learning, Spectral Variables and Irrigation Management

by Fábio Henrique Rojo Baio, Dthenifer Cordeiro Santana, Larissa Pereira Ribeiro Teodoro, Izabela Cristina de Oliveira, Ricardo Gava, João Lucas Gouveia de Oliveira, Carlos Antonio da Silva Junior, Paulo Eduardo Teodoro and Luciano Shozo Shiratsuchi

Remote Sens. 2023, 15(1), 79; https://doi.org/10.3390/rs15010079 - 23 Dec 2022

Cited by 10 | Viewed by 3328

Abstract

Predicting maize yield using spectral information, temperature, and different irrigation management through machine learning algorithms provide information in a fast, accurate, and non-destructive way. The use of multispectral sensor data coupled with irrigation management in maize allows further exploration of water behavior and its relationship with changes in spectral bands presented by the crop. Thus, the objective of this study was to evaluate, by means of multivariate statistics and machine learning techniques, the relationship between irrigation management and spectral bands in predicting maize yields. Field experiments were carried out over two seasons (first and second seasons) in a randomized block design with four treatments (control and three additional irrigation levels) and eighteen sample repetitions. The response variables analyzed were vegetation indices (IVs) and crop yield (GY). Measurement of spectral wavelengths was performed with the Sensefly eBee RTK, with autonomous flight control. The eBee was equipped with the Parrot Sequoia multispectral sensor acquiring reflectance at the wavelengths of green (550 nm ± 40 nm), red (660 nm ± 40 nm), red-edge (735 nm ± 10 nm), and NIR (790 nm ± 40 nm). The blue length (496 nm) was obtained by additional RGB imaging. Data were subjected to Pearson correlations (r) between the evaluated variables represented by a correlation and scatter plot. Subsequently, the canonical analysis was performed to verify the interrelationship between the variables evaluated. Data were also subjected to machine learning (ML) analysis, in which three different input dataset configurations were tested: using only irrigation management (IR), using irrigation management and spectral bands (SB+IR), and using irrigation management, spectral bands, and temperature (IR+SB+Temp). ML models used were: Artificial Neural Network (ANN), M5P Decision Tree (J48), REPTree Decision Tree (REPT), Random Forest (RF), and Support Vector Machine (SVM). A multiple linear regression (LR) was tested as a control model. Our results revealed that Random Forest has higher accuracy in predicting grain yield in maize, especially when associated with the inputs SB+IR and SB+IR+Temp. Full article

(This article belongs to the Special Issue High-Throughput Phenotyping in Plants Using Remote Sensing)

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