Combining UAV-RGB high-throughput field phenotyping and genome-wide association study to reveal genetic variation of rice germplasms in dynamic response to drought stress

Modern phenomics to empower holistic crop science, agronomy, and breeding research

Crop phenomics enables the collection of diverse plant traits for a large number of samples along different time scales, representing a greater data collection throughput compared with traditional measurements. Most modern crop phenomics use different sensors to collect reflective, emitted, and fluorescence signals, etc., from plant organs at different spatial and temporal resolutions. Such multi-modal, high-dimensional data not only accelerates basic research on crop physiology, genetics, and whole plant systems modeling, but also supports the optimization of field agronomic practices, internal environments of plant factories, and ultimately crop breeding. Major challenges and opportunities facing the current crop phenomics research community include developing community consensus or standards for data collection, management, sharing, and processing, developing capabilities to measure physiological parameters, and enabling farmers and breeders to effectively use phenomics in the field to directly support agricultural production.

Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize

Field-based phenomic prediction employs novel features, like vegetation indices (VIs) from drone images, to predict key agronomic traits in maize, despite challenges in matching biomarker measurement time points across years or environments. This study utilized functional principal component analysis (FPCA) to summarize the variation of temporal VIs, uniquely allowing the integration of this data into phenomic prediction models tested across multiple years (2018-2021) and environments. The models, which included 1 genomic, 2 phenomic, 2 multikernel, and 1 multitrait type, were evaluated in 4 prediction scenarios (CV2, CV1, CV0, and CV00), relevant for plant breeding programs, assessing both tested and untested genotypes in observed and unobserved environments. Two hybrid populations (415 and 220 hybrids) demonstrated the visible atmospherically resistant index's strong temporal correlation with grain yield (up to 0.59) and plant height. The first 2 FPCAs explained 59.3 ± 13.9% and 74.2 ± 9.0% of the temporal variation of temporal data of VIs, respectively, facilitating predictions where flight times varied. Phenomic data, particularly when combined with genomic data, often were comparable to or numerically exceeded the base genomic model in prediction accuracy, particularly for grain yield in untested hybrids, although no significant differences in these models' performance were consistently observed. Overall, this approach underscores the effectiveness of FPCA and combined models in enhancing the prediction of grain yield and plant height across environments and diverse agricultural settings.

Common and specific genetic basis of metabolite-mediated drought responses in rice

Plants orchestrate drought responses at metabolic level but the genetic basis remains elusive in rice. In this study, 233 drought-responsive metabolites (DRMs) were quantified in a large rice population comprised of 510 diverse accessions at the reproductive stage. Large metabolic variations in drought responses were detected, and little correlation of metabolic levels between drought and normal conditions were observed. Interestingly, most of these DRMs could predict drought resistance in high accuracy. Genome-wide association study revealed 2522 significant association signals for 233 DRMs, and 98% (2471/2522) of the signals were co-localized with the association loci for drought-related phenotypic traits in the same population or the linkage-mapped QTLs for drought resistance in other populations. Totally, 10 candidate genes were efficiently identified for nine DRMs, seven of which harbored cis-eQTLs under drought condition. Based on comparative GWAS of common DRMs in rice and maize, representing irrigated and upland crops, we have identified three pairs of homologous genes associated with three DRMs between the two crops. Among the homologous genes, a transferase gene responsible for metabolic variation of N-feruloylputrescine was confirmed to confer enhanced drought resistance in rice. Our study provides not only genetic architecture of metabolic responses to drought stress in rice but also metabolic data resources to reveal the common and specific metabolite-mediated drought responses in different crops.

The Dissection of Nitrogen Response Traits Using Drone Phenotyping and Dynamic Phenotypic Analysis to Explore N Responsiveness and Associated Genetic Loci in Wheat

Inefficient nitrogen (N) utilization in agricultural production has led to many negative impacts such as excessive use of N fertilizers, redundant plant growth, greenhouse gases, long-lasting toxicity in ecosystem, and even effect on human health, indicating the importance to optimize N applications in cropping systems. Here, we present a multiseasonal study that focused on measuring phenotypic changes in wheat plants when they were responding to different N treatments under field conditions. Powered by drone-based aerial phenotyping and the AirMeasurer platform, we first quantified 6 N response-related traits as targets using plot-based morphological, spectral, and textural signals collected from 54 winter wheat varieties. Then, we developed dynamic phenotypic analysis using curve fitting to establish profile curves of the traits during the season, which enabled us to compute static phenotypes at key growth stages and dynamic phenotypes (i.e., phenotypic changes) during N response. After that, we combine 12 yield production and N-utilization indices manually measured to produce N efficiency comprehensive scores (NECS), based on which we classified the varieties into 4 N responsiveness (i.e., N-dependent yield increase) groups. The NECS ranking facilitated us to establish a tailored machine learning model for N responsiveness-related varietal classification just using N-response phenotypes with high accuracies. Finally, we employed the Wheat55K SNP Array to map single-nucleotide polymorphisms using N response-related static and dynamic phenotypes, helping us explore genetic components underlying N responsiveness in wheat. In summary, we believe that our work demonstrates valuable advances in N response-related plant research, which could have major implications for improving N sustainability in wheat breeding and production.

Leaf rolling precedes stomatal closure in rice (Oryza sativa) under drought conditions

Leaf rolling is a physiological response to drought that may help to reduce water loss, but its significance as a contribution to drought tolerance is uncertain. We scored the leaf rolling of four rice genotypes along an experimental drought gradient using an improved cryo-microscopy method. Leaf water potential (Ψleaf), gas exchange, chlorophyll fluorescence, leaf hydraulic conductance, rehydration capacity, and the bulk turgor loss point were also analysed. During the drought process, stomatal conductance declined sharply to reduce water loss, and leaves rolled up before the stomata completely closed. The leaf water loss rate of rolled leaves was significantly reduced compared with artificially flattened leaves. The Ψleaf threshold of initial leaf rolling ranged from -1.95 to -1.04 MPa across genotypes. When a leaf rolled so that the leaf edges were touching, photosynthetic rate and stomatal conductance declined more than 80%. Across genotypes, leaf hydraulic conductance declined first, followed by gas exchange and chlorophyll fluorescence parameters. However, the Ψleaf threshold for a given functional trait decline differed significantly among genotypes, with the exception of leaf hydraulic conductance. Our results suggested that leaf rolling was mechanistically linked to drought avoidance and tolerance traits and might serve as a useful phenotypic trait for rice breeding in future drought scenarios.

Phytohormone Response of Drought-Acclimated Illicium difengpi (Schisandraceae)

Illicium difengpi (Schisandraceae), which is an endemic, medicinal, and endangered species found in small and isolated populations that inhabit karst mountain areas, has evolved strategies to adapt to arid environments and is thus an excellent material for exploring the mechanisms of tolerance to severe drought. In experiment I, I. difengpi plants were subjected to three soil watering treatments (CK, well-watered treatment at 50% of the dry soil weight for 18 days; DS, drought stress treatment at 10% of the dry soil weight for 18 days; DS-R, drought-rehydration treatment at 10% of the dry soil weight for 15 days followed by rewatering to 50% of the dry soil weight for another 3 days). The effects of the drought and rehydration treatments on leaf succulence, phytohormones, and phytohormonal signal transduction in I. difengpi plants were investigated. In experiment II, exogenous abscisic acid (ABA, 60 mg L-1) and zeatin riboside (ZR, 60 mg L-1) were sprayed onto DS-treated plants to verify the roles of exogenous phytohormones in alleviating drought injury. Leaf succulence showed marked changes in response to the DS and DS-R treatments. The relative concentrations of ABA, methyl jasmonate (MeJA), salicylic acid glucoside (SAG), and cis-zeatin riboside (cZR) were highly correlated with relative leaf succulence. The leaf succulence of drought-treated I. difengpi plants recovered to that observed with the CK treatment after exogenous application of ABA or ZR. Differentially expressed genes involved in biosynthesis and signal transduction of phytohormones (ABA and JA) in response to drought stress were identified by transcriptomic profiling. The current study suggested that the phytohormones ABA, JA, and ZR may play important roles in the response to severe drought and provides a preliminary understanding of the physiological mechanisms involved in phytohormonal regulation in I. difengpi, an endemic, medicinal, and highly drought-tolerant plant found in extremely small populations in the karst region of South China.

An overview of image-based phenotyping as an adaptive 4.0 technology for studying plant abiotic stress: A bibliometric and literature review

Improving the tolerance of crop species to abiotic stresses that limit plant growth and productivity is essential for mitigating the emerging problems of global warming. In this context, imaged data analysis represents an effective method in the 4.0 technology era, where this method has the non-destructive and recursive characterization of plant phenotypic traits as selection criteria. So, the plant breeders are helped in the development of adapted and climate-resilient crop varieties. Although image-based phenotyping has recently resulted in remarkable improvements for identifying the crop status under a range of growing conditions, the topic of its application for assessing the plant behavioral responses to abiotic stressors has not yet been extensively reviewed. For such a purpose, bibliometric analysis is an ideal analytical concept to analyze the evolution and interplay of image-based phenotyping to abiotic stresses by objectively reviewing the literature in light of existing database. Bibliometricy, a bibliometric analysis was applied using a systematic methodology which involved data mining, mining data improvement and analysis, and manuscript construction. The obtained results indicate that there are 554 documents related to image-based phenotyping to abiotic stress until 5 January 2023. All document showed the future development trends of image-based phenotyping will be mainly centered in the United States, European continent and China. The keywords analysis major focus to the application of 4.0 technology and machine learning in plant breeding, especially to create the tolerant variety under abiotic stresses. Drought and saline become an abiotic stress often using image-based phenotyping. Besides that, the rice, wheat and maize as the main commodities in this topic. In conclusion, the present work provides information on resolutive interactions in developing image-based phenotyping to abiotic stress, especially optimizing high-throughput sensors in image-based phenotyping for the future development.

UAV-based time-series phenotyping reveals the genetic basis of plant height in upland cotton

Plant height (PH) is an important agronomic trait affecting crop architecture, biomass, resistance to lodging and mechanical harvesting. Elucidating the genetic governance of plant height is crucial because of the global demand for high crop yields. However, during the rapid growth period of plants the PH changes a lot on a daily basis, which makes it difficult to accurately phenotype the trait by hand on a large scale. In this study, an unmanned aerial vehicle (UAV)-based remote-sensing phenotyping platform was applied to obtain time-series PHs of 320 upland cotton accessions in three different field trials. The results showed that the PHs obtained from UAV images were significantly correlated with ground-based manual measurements, for three trials (R2  = 0.96, 0.95 and 0.96). Two genetic loci on chromosomes A01 and A11 associated with PH were identified by genome-wide association studies (GWAS). GhUBP15 and GhCUL1 were identified to influence PH in further analysis. We obtained a time series of PH values for three field conditions based on remote sensing with UAV. The key genes identified in this study are of great value for the breeding of ideal plant architecture in cotton.

Fantastic genes: where and how to find them? Exploiting rice genetic resources for the improvement of yield, tolerance, and resistance to a wide array of stresses in rice

Rice production is a critical component of global food security. To date, rice is grown in over 100 countries and is the primary source of food for more than 3 billion people. Despite its importance, rice production is facing numerous challenges that threaten its future viability. One of the primary problems is the advent of climate change. The changing climatic conditions greatly affect the growth and productivity of rice crop and the quality of rice yield. Similarly, biotic stresses brought about by pathogen and pest infestations are greatly affecting the productivity of rice. To address these issues, the utilization of rice genetic resources is necessary to map, identify, and understand the genetics of important agronomic traits. This review paper highlights the role of rice genetic resources for developing high-yielding and stress-tolerant rice varieties. The integration of genetic, genomic, and phenomic tools in rice breeding programs has led to the development of high-yielding and stress-tolerant rice varieties. The collaboration of multidisciplinary teams of experts, sustainable farming practices, and extension services for farmers is essential for accelerating the development of high-yielding and stress-tolerant rice varieties.

Recent advances in artificial intelligence, mechanistic models, and speed breeding offer exciting opportunities for precise and accelerated genomics-assisted breeding

Given the challenges of population growth and climate change, there is an urgent need to expedite the development of high-yielding stress-tolerant crop cultivars. While traditional breeding methods have been instrumental in ensuring global food security, their efficiency, precision, and labour intensiveness have become increasingly inadequate to address present and future challenges. Fortunately, recent advances in high-throughput phenomics and genomics-assisted breeding (GAB) provide a promising platform for enhancing crop cultivars with greater efficiency. However, several obstacles must be overcome to optimize the use of these techniques in crop improvement, such as the complexity of phenotypic analysis of big image data. In addition, the prevalent use of linear models in genome-wide association studies (GWAS) and genomic selection (GS) fails to capture the nonlinear interactions of complex traits, limiting their applicability for GAB and impeding crop improvement. Recent advances in artificial intelligence (AI) techniques have opened doors to nonlinear modelling approaches in crop breeding, enabling the capture of nonlinear and epistatic interactions in GWAS and GS and thus making this variation available for GAB. While statistical and software challenges persist in AI-based models, they are expected to be resolved soon. Furthermore, recent advances in speed breeding have significantly reduced the time (3-5-fold) required for conventional breeding. Thus, integrating speed breeding with AI and GAB could improve crop cultivar development within a considerably shorter timeframe while ensuring greater accuracy and efficiency. In conclusion, this integrated approach could revolutionize crop breeding paradigms and safeguard food production in the face of population growth and climate change.

CropQuant-Air: an AI-powered system to enable phenotypic analysis of yield- and performance-related traits using wheat canopy imagery collected by low-cost drones

As one of the most consumed stable foods around the world, wheat plays a crucial role in ensuring global food security. The ability to quantify key yield components under complex field conditions can help breeders and researchers assess wheat's yield performance effectively. Nevertheless, it is still challenging to conduct large-scale phenotyping to analyse canopy-level wheat spikes and relevant performance traits, in the field and in an automated manner. Here, we present CropQuant-Air, an AI-powered software system that combines state-of-the-art deep learning (DL) models and image processing algorithms to enable the detection of wheat spikes and phenotypic analysis using wheat canopy images acquired by low-cost drones. The system includes the YOLACT-Plot model for plot segmentation, an optimised YOLOv7 model for quantifying the spike number per m² (SNpM²) trait, and performance-related trait analysis using spectral and texture features at the canopy level. Besides using our labelled dataset for model training, we also employed the Global Wheat Head Detection dataset to incorporate varietal features into the DL models, facilitating us to perform reliable yield-based analysis from hundreds of varieties selected from main wheat production regions in China. Finally, we employed the SNpM² and performance traits to develop a yield classification model using the Extreme Gradient Boosting (XGBoost) ensemble and obtained significant positive correlations between the computational analysis results and manual scoring, indicating the reliability of CropQuant-Air. To ensure that our work could reach wider researchers, we created a graphical user interface for CropQuant-Air, so that non-expert users could readily use our work. We believe that our work represents valuable advances in yield-based field phenotyping and phenotypic analysis, providing useful and reliable toolkits to enable breeders, researchers, growers, and farmers to assess crop-yield performance in a cost-effective approach.

Metabolite-Based Genome-Wide Association Studies of Large-Scale Metabolome Analysis to Illustrate Alterations in the Metabolite Landscape of Plants upon Responses to Stresses

Given that anthropogenic activities are evoking a profound effect on the climate resulting in more extreme events such as severe drought and heat waves while global demand for food is ever-increasing, understanding plant responses to stresses is critical. As metabolites are fundamental for plant growth regulation and plant lifespan and an important component of yield, illustrating how the metabolite landscape of plant changes following stress will supply important clues as to how to improve the plant resistance to stress. Recently, billions of single-nucleotide polymorphisms (SNPs) have been obtained and used to identify the associations between genetic variants of genomes and relevant crop agronomic traits through different genetic methods such as genome-wide association studies (GWAS). Therefore, in this chapter, we provide comprehensive guidelines concerning the experimental design, metabolite profiling, and metabolite-based genome-wide association studies (mGWAS) of large-scale metabolome analysis to accelerate the future identification of the valuable stress-resistant genes and metabolites.

Prediction of heading date, culm length, and biomass from canopy-height-related parameters derived from time-series UAV observations of rice

Unmanned aerial vehicles (UAVs) are powerful tools for monitoring crops for high-throughput phenotyping. Time-series aerial photography of fields can record the whole process of crop growth. Canopy height (CH), which is vertical plant growth, has been used as an indicator for the evaluation of lodging tolerance and the prediction of biomass and yield. However, there have been few attempts to use UAV-derived time-series CH data for field testing of crop lines. Here we provide a novel framework for trait prediction using CH data in rice. We generated UAV-based digital surface models of crops to extract CH data of 30 Japanese rice cultivars in 2019, 2020, and 2021. CH-related parameters were calculated in a non-linear time-series model as an S-shaped plant growth curve. The maximum saturation CH value was the most important predictor for culm length. The time point at the maximum CH contributed to the prediction of days to heading, and was able to predict stem and leaf weight and aboveground weight, possibly reflecting the association of biomass with duration of vegetative growth. These results indicate that the CH-related parameters acquired by UAV can be useful as predictors of traits typically measured by hand.

Panicle Ratio Network: streamlining rice panicle measurement by deep learning with ultra-high-definition aerial images in the field

The heading date and effective tiller percentage are important traits in rice, and they directly affect plant architecture and yield. Both traits are related to the ratio of the panicle number to the maximum tiller number, referred to as the panicle ratio (PR). In this study, an automatic PR estimation model (PRNet) based on a deep convolutional neural network was developed. Ultra-high-definition unmanned aerial vehicle (UAV) images were collected from cultivated rice varieties planted in 2384 experimental plots in 2019 and 2020 and in a large field in 2021. The determination coefficient between estimated PR and ground-measured PR reached 0.935, and the root mean square error values for the estimations of the heading date and effective tiller percentage were 0.687 d and 4.84%, respectively. Based on the analysis of the results, various factors affecting PR estimation and strategies for improving PR estimation accuracy were investigated. The satisfactory results obtained in this study demonstrate the feasibility of using UAVs and deep learning techniques to replace ground-based manual methods to accurately extract phenotypic information of crop micro targets (such as grains per panicle, panicle flowering, etc.) for rice and potentially for other cereal crops in future research.

AirMeasurer: open-source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice

Low-altitude aerial imaging, an approach that can collect large-scale plant imagery, has grown in popularity recently. Amongst many phenotyping approaches, unmanned aerial vehicles (UAVs) possess unique advantages as a consequence of their mobility, flexibility and affordability. Nevertheless, how to extract biologically relevant information effectively has remained challenging. Here, we present AirMeasurer, an open-source and expandable platform that combines automated image analysis, machine learning and original algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low-cost UAVs in rice (Oryza sativa) trials. We applied the platform to study hundreds of rice landraces and recombinant inbred lines at two sites, from 2019 to 2021. A range of static and dynamic traits were quantified, including crop height, canopy coverage, vegetative indices and their growth rates. After verifying the reliability of AirMeasurer-derived traits, we identified genetic variants associated with selected growth-related traits using genome-wide association study and quantitative trait loci mapping. We found that the AirMeasurer-derived traits had led to reliable loci, some matched with published work, and others helped us to explore new candidate genes. Hence, we believe that our work demonstrates valuable advances in aerial phenotyping and automated 2D/3D trait analysis, providing high-quality phenotypic information to empower genetic mapping for crop improvement.

Hyperspectral remote sensing to assess weed competitiveness in maize farmland ecosystems

Weed competition causes serious economic losses to maize production. Timely and accurate assessment of pressure from competition is crucial for ecological weed management. In this work, we apply hyperspectral remote sensing (HRS) technology to conduct a competitive experiment in Harbin, China, in 2021, with 5-leaf maize as the study target. A weed competition assessment method that combines comprehensive competition indices (CCI) and deep learning is proposed. For the comprehensive competition assessment, the relationship between different weed competitive pressures (Levels 1-5) and changes in the structural and physiological information of maize was analyzed. The accumulative/transient competition indices CCI-A and CCI-T were designed for accurate quantification. The results showed that parameters such as plant height, stalk thickness and nutrient elements of maize decreased with increasing competition level. Parameters, such as stomatal conductance and transpiration rate, showed a fluctuating change of increasing and then decreasing with increasing competition level. Compared with the traditional relative competitive intensity (RCI), the standard deviation of CCI is 0.303 and 0.499. The dispersion effect of CCI is better and more suitable for quantifying the competition response. HRS images combined with 3D-CNN model were then applied to reveal the spectral response to different weed competition pressures (Levels 1-5) and to make early predictions of weed competition. The first-order derivative showed that the spectral reflectance exhibited significant differences at 520-525 nm peak, 570-655 nm trough, and near 700 nm red edge. For hyperspectral spatial-spectral features, the 3D-CNN model is proposed for prediction of competing indices CCI. In addition, the VIP method is used to select the characteristic wavelengths. The 3D-CNN model achieves a prediction accuracy of RMSE = 0.106 and 0.152 using 13 feature bands, which can accurately quantify the subtle changes in competition indices. Overall, this study shows that the combination of CCI and deep learning can provide a multivariate and comprehensive assessment of weed competition pressure.

High-throughput unmanned aerial vehicle-based phenotyping provides insights into the dynamic process and genetic basis of rapeseed waterlogging response in the field

Waterlogging severely affects the growth, development, and yield of crops. Accurate high-throughput phenotyping is important for exploring the dynamic crop waterlogging response in the field, and the genetic basis of waterlogging tolerance. In this study, a multi-model remote sensing phenotyping platform based on an unmanned aerial vehicle (UAV) was used to assess the genetic response of rapeseed (Brassica napus) to waterlogging, by measuring morphological traits and spectral indices over 2 years. The dynamic responses of the morphological and spectral traits indicated that the rapeseed waterlogging response was severe before the middle stage within 18 d after recovery, but it subsequently decreased partly. Genome-wide association studies identified 289 and 333 loci associated with waterlogging tolerance in 2 years. Next, 25 loci with at least nine associations with waterlogging-related traits were defined as highly reliable loci, and 13 loci were simultaneously identified by waterlogging tolerance coefficients of morphological traits, spectral indices, and common factors. Forty candidate genes were predicted in the regions of 13 overlapping loci. Our study provides insights into the understanding of the dynamic process and genetic basis of rapeseed waterlogging response in the field by a high-throughput UAV phenotyping platform. The highly reliable loci identified in this study are valuable for breeding waterlogging-tolerant rapeseed cultivars.

Bringing more players into play: Leveraging stress in genome wide association studies

In order to meet the demand of the burgeoning human population as well as to adapt crops to the enhanced abiotic and biotic stress caused by the global climatic change, breeders focus on identifying valuable genes to improve both crop stress tolerance and crop quality. Recently, with the development of next-generation sequencing methods, millions of high quality single-nucleotide polymorphisms (SNPs) have been made available and genome-wide association studies (GWAS) are widely used in crop improvement studies to identify the associations between genetic variants of genomes and relevant crop agronomic traits. Here, we review classic cases of use of GWAS to identify genetic variants associated with valuable traits such as geographic adaptation, crop quality and metabolites. We discuss the power of stress GWAS to identify further associations including those with genes that are not, or only lowly, expressed during optimal growth conditions. Finally, we emphasize recent demonstrations of the efficiency and accuracy of time-resolved dynamic stress GWAS and GWAS based on genomic gene expression and structural variations, which can be applied to resolve more comprehensively the genetic regulation mechanisms of complex traits.

Advances in optical phenotyping of cereal crops

Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories - morphological, biochemical, physiological, and performance traits - and illustrate appropriate sensors for each extraction. In addition to the current status, we discuss the challenges of OSP and provide possible solutions. We propose that optical sensing-based traits need to be explored further, and that standardization of the language of phenotyping and worldwide collaboration between phenotyping researchers and other fields need to be established.

Identification and Comprehensive Evaluation of Resistant Weeds Using Unmanned Aerial Vehicle-Based Multispectral Imagery

Atrazine is one of the most widely used herbicides in weed management. However, the widespread use of atrazine has concurrently accelerated the evolution of weed resistance mechanisms. Resistant weeds were identified early to contribute to crop protection in precision agriculture before visible symptoms of atrazine application to weeds in actual field environments. New developments in unmanned aerial vehicle (UAV) platforms and sensor technologies promote cost-effective data collection by collecting multi-modal data at very high spatial and spectral resolution. In this study, we obtained multispectral and RGB images using UAVs, increased available information with the help of image fusion technology, and developed a weed spectral resistance index, WSRI = (RE-R)/(RE-B), based on the difference between susceptible and resistant weed biotypes. A deep convolutional neural network (DCNN) was applied to evaluate the potential for identifying resistant weeds in the field. Comparing the WSRI introduced in this study with previously published vegetation indices (VIs) shows that the WSRI is better at classifying susceptible and resistant weed biotypes. Fusing multispectral and RGB images improved the resistance identification accuracy, and the DCNN achieved high field accuracies of 81.1% for barnyardgrass and 92.4% for velvetleaf. Time series and weed density influenced the study of weed resistance, with 4 days after application (4DAA) identified as a watershed timeframe in the study of weed resistance, while different weed densities resulted in changes in classification accuracy. Multispectral and deep learning proved to be effective phenotypic techniques that can thoroughly analyze weed resistance dynamic response and provide valuable methods for high-throughput phenotyping and accurate field management of resistant weeds.