Global warming, climate change, and environmental pollution present plants with unique combinations of different abiotic and biotic stresses. Although much is known about how plants acclimate to each ...of these individual stresses, little is known about how they respond to a combination of many of these stress factors occurring together, namely a multifactorial stress combination. Recent studies revealed that increasing the number of different co-occurring multifactorial stress factors causes a severe decline in plant growth and survival, as well as in the microbiome biodiversity that plants depend upon. This effect should serve as a dire warning to our society and prompt us to decisively act to reduce pollutants, fight global warming, and augment the tolerance of crops to multifactorial stress combinations.
A multifactorial stress combination occurs when more than two to three abiotic and/or biotic stress factors simultaneously impact a plant.Global warming, climate change, and industrial pollution could result in an increase in the frequency, complexity, and intensity of multifactorial stress combinations impacting plants, soils, and microbial communities.With the increase in the number of factors simultaneously impacting plants, the survival and growth of plants declines, even if the levels of each of these individual stresses is very low.The response of plants to a multifactorial stress combination is unique and involves many transcripts and genes that are not altered in response to each of the different stresses applied individually.The harmful effects of a multifactorial stress combination on the survival and growth of plants, different soil properties, and diversity of microbial communities should serve as a dire warning to our society and prompt us to act drastically to reduce the different sources of multifactorial stresses in our environment.
Preharvest crop yield prediction is critical for grain policy making and food security. Early estimation of yield at field or plot scale also contributes to high-throughput plant phenotyping and ...precision agriculture. New developments in Unmanned Aerial Vehicle (UAV) platforms and sensor technology facilitate cost-effective data collection through simultaneous multi-sensor/multimodal data collection at very high spatial and spectral resolutions. The objective of this study is to evaluate the power of UAV-based multimodal data fusion using RGB, multispectral and thermal sensors to estimate soybean (Glycine max) grain yield within the framework of Deep Neural Network (DNN). RGB, multispectral, and thermal images were collected using a low-cost multi-sensory UAV from a test site in Columbia, Missouri, USA. Multimodal information, such as canopy spectral, structure, thermal and texture features, was extracted and combined to predict crop grain yield using Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), Support Vector Regression (SVR), input-level feature fusion based DNN (DNN-F1) and intermediate-level feature fusion based DNN (DNN-F2). The results can be summarized in three messages: (1) multimodal data fusion improves the yield prediction accuracy and is more adaptable to spatial variations; (2) DNN-based models improve yield prediction model accuracy: the highest accuracy was obtained by DNN-F2 with an R2 of 0.720 and a relative root mean square error (RMSE%) of 15.9%; (3) DNN-based models were less prone to saturation effects, and exhibited more adaptive performance in predicting grain yields across the Dwight, Pana and AG3432 soybean genotypes in our study. Furthermore, DNN-based models demonstrated consistent performance over space with less spatial dependency and variations. This study indicates that multimodal data fusion using low-cost UAV within a DNN framework can provide a relatively accurate and robust estimation of crop yield, and deliver valuable insight for high-throughput phenotyping and crop field management with high spatial precision.
•A low-cost multi-sensor UAV for crop monitoring & phenotyping was developed.•Canopy structure, temperature and texture are important features for yield model.•Multimodal data fusion showed effectiveness in yield prediction.•DNN provided promising results in yield prediction across genotypes and over space.
Summary
Climate change‐driven extreme weather events, combined with increasing temperatures, harsh soil conditions, low water availability and quality, and the introduction of many man‐made ...pollutants, pose a unique challenge to plants. Although our knowledge of the response of plants to each of these individual conditions is vast, we know very little about how a combination of many of these factors, occurring simultaneously, that is multifactorial stress combination, impacts plants.
Seedlings of wild‐type and different mutants of Arabidopsis thaliana plants were subjected to a multifactorial stress combination of six different stresses, each applied at a low level, and their survival, physiological and molecular responses determined.
Our findings reveal that, while each of the different stresses, applied individually, had a negligible effect on plant growth and survival, the accumulated impact of multifactorial stress combination on plants was detrimental. We further show that the response of plants to multifactorial stress combination is unique and that specific pathways and processes play a critical role in the acclimation of plants to multifactorial stress combination.
Taken together our findings reveal that further polluting our environment could result in higher complexities of multifactorial stress combinations that in turn could drive a critical decline in plant growth and survival.
Episodes of prolonged drought coupled with heat waves (i.e. drought and heat combination) can have a devastating impact on agricultural production and crop yield. It is therefore not surprising that ...improving tolerance to drought and heat combination has been a major goal for breeders and biotech companies. Although much is known about the physiological and molecular responses of vegetative tissues to a combination of drought and heat stress, less is known about the impact of this stress combination on yield and different yield components. Here, we used a meta‐analysis approach to synthesize results from over 120 published case studies of crop responses to combined drought and heat stress. Our findings reveal that drought and heat stress combination significantly impacts yield by decreasing harvest index, shortening the life cycle of crops, and altering seed number, size and composition. Furthermore, these impacts are more severe when the stress combination is applied during the reproductive stage of plants. We further identify differences in how legumes and cereals respond to the stress combination and reveal that utilizing C3 or C4 metabolism may not provide an advantage to plants during stress combinations. Taken together our study highlights a need to focus future studies, as well as breeding efforts, on crop responses to drought and heat combination at the reproductive stage of different crop species.
The response of plants to a combination of two or more different stresses requires complex physiological and transcriptomic responses that are mediated by multiple transcription factor networks.
...Abstract
Episodes of heat waves combined with drought can have a devastating impact on agricultural production worldwide. These conditions, as well as many other types of stress combinations, impose unique physiological and developmental demands on plants and require the activation of dedicated pathways. Here, we review recent RNA sequencing studies of stress combination in plants, and conduct a meta-analysis of the transcriptome response of plants to different types of stress combination. Our analysis reveals that each different stress combination is accompanied by its own set of stress combination-specific transcripts, and that the response of different transcription factor families is unique to each stress combination. The alarming rate of increase in global temperatures, coupled with the predicted increase in future episodes of extreme weather, highlight an urgent need to develop crop plants with enhanced tolerance to stress combination. The uniqueness and complexity of the physiological and molecular response of plants to each different stress combination, highlighted here, demonstrate the daunting challenge we face in accomplishing this goal. Dedicated efforts combining field experimentation, omics, and network analyses, coupled with advanced phenotyping and breeding methods, will be needed to address specific crops and particular stress combinations relevant to maintaining our future food chain secured.
In this paper, a new robotic architecture for plant phenotyping is being introduced. The architecture consists of two robotic platforms: an autonomous ground vehicle (Vinobot) and a mobile ...observation tower (Vinoculer). The ground vehicle collects data from individual plants, while the observation tower oversees an entire field, identifying specific plants for further inspection by the Vinobot. The advantage of this architecture is threefold: first, it allows the system to inspect large areas of a field at any time, during the day and night, while identifying specific regions affected by biotic and/or abiotic stresses; second, it provides high-throughput plant phenotyping in the field by either comprehensive or selective acquisition of accurate and detailed data from groups or individual plants; and third, it eliminates the need for expensive and cumbersome aerial vehicles or similarly expensive and confined field platforms. As the preliminary results from our algorithms for data collection and 3D image processing, as well as the data analysis and comparison with phenotype data collected by hand demonstrate, the proposed architecture is cost effective, reliable, versatile, and extendable.
In the field, plants experience high light (HL) intensities that are often accompanied by elevated temperatures. Such conditions are a serious threat to agriculture production, because photosynthesis ...is highly sensitive to both HL intensities and high-temperature stress. One of the potential cellular targets of HL and heat stress (HS) combination is PSII because its degree of photoinhibition depends on the balance between the rate of PSII damage (induced by light stress), and the rate of PSII repair (impaired under HS). Here, we studied the responses of Arabidopsis (
) plants to a combination of HL and HS (HL+HS) conditions. Combined HL+HS was accompanied by irreversible damage to PSII, decreased D1 (PsbA) protein levels, and an enhanced transcriptional response indicative of PSII repair activation. We further identified several unique aspects of this stress combination that included enhanced accumulation of jasmonic acid (JA) and JA-Ile, elevated expression of over 2,200 different transcripts that are unique to the stress combination (including many that are JA-associated), and distinctive structural changes to chloroplasts. A mutant deficient in JA biosynthesis (allene oxide synthase) displayed enhanced sensitivity to combined HL+HS and further analysis revealed that JA is required for regulating several transcriptional responses unique to the stress combination. Our study reveals that JA plays an important role in the acclimation of plants to a combination of HL+HS.
•The co-occurrence of heat and water-deficit stress during flowering negatively impacts crop productivity.•Heat, water-deficit, and their combination alter developmental processes associated with ...plant reproduction.•Stress-induced changes in sugar metabolism, reactive oxygen species and hormone levels are thought to play a role in yield reduction during stress combination.•Understanding the molecular processes associated with yield reduction during stress combination would allow the development of climate-resilient crops.
Historically, extended droughts combined with heat waves caused severe reductions in crop yields estimated at billions of dollars annually. Because global warming and climate change are driving an increase in the frequency and intensity of combined water-deficit and heat stress episodes, understanding how these episodes impact yield is critical for our efforts to develop climate change-resilient crops. Recent studies demonstrated that a combination of water-deficit and heat stress exacerbates the impacts of water-deficit or heat stress on reproductive processes of different cereals and legumes, directly impacting grain production. These studies identified several different mechanisms potentially underlying the effects of stress combination on anthers, pollen, and stigma development and function, as well as fertilization. Here we review some of these findings focusing on unbalanced reactive oxygen accumulation, altered sugar concentrations, and conflicting functions of different hormones, as contributing to the reduction in yield during a combination of water-deficit and heat stress. Future studies focused on the effects of water-deficit and heat stress combination on reproduction of different crops are likely to unravel additional mechanisms, as well as reveal novel ways to develop stress combination-resilient crops. These could mitigate some of the potentially devastating impacts of this stress combination on agriculture.
Non-destructive crop monitoring over large areas with high efficiency is of great significance in precision agriculture and plant phenotyping, as well as decision making with regards to grain policy ...and food security. The goal of this research was to assess the potential of combining canopy spectral information with canopy structure features for crop monitoring using satellite/unmanned aerial vehicle (UAV) data fusion and machine learning. Worldview-2/3 satellite data were tasked synchronized with high-resolution RGB image collection using an inexpensive unmanned aerial vehicle (UAV) at a heterogeneous soybean (Glycine max (L.) Merr.) field. Canopy spectral information (i.e., vegetation indices) was extracted from Worldview-2/3 data, and canopy structure information (i.e., canopy height and canopy cover) was derived from UAV RGB imagery. Canopy spectral and structure information and their combination were used to predict soybean leaf area index (LAI), aboveground biomass (AGB), and leaf nitrogen concentration (N) using partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) with a newly proposed activation function. The results revealed that: (1) UAV imagery-derived high-resolution and detailed canopy structure features, canopy height, and canopy coverage were significant indicators for crop growth monitoring, (2) integration of satellite imagery-based rich canopy spectral information with UAV-derived canopy structural features using machine learning improved soybean AGB, LAI, and leaf N estimation on using satellite or UAV data alone, (3) adding canopy structure information to spectral features reduced background soil effect and asymptotic saturation issue to some extent and led to better model performance, (4) the ELR model with the newly proposed activated function slightly outperformed PLSR, RFR, and SVR in the prediction of AGB and LAI, while RFR provided the best result for N estimation. This study introduced opportunities and limitations of satellite/UAV data fusion using machine learning in the context of crop monitoring.
Accurately mapping heterogeneous agricultural landscape is an important prerequisite for agricultural field management (e.g., weed control), plant phenotyping and yield prediction, as well as ...ecological characterization. Compared to traditional mapping practices that require intensive field surveys, remote sensing technologies offer efficient and cost-effective means for crop type mapping from regional to global scales. However, mapping heterogeneous agricultural landscape is a challenge because of diverse and complex spectral profiles of crops. We propose a novel deep learning method, namely deep progressively expanded network (dPEN), for mapping nineteen different objects including crop types, weeds and crop residues, in a heterogeneous agricultural field using WorldView-3 (WV-3) imagery. To assess the mapping accuracy of dPEN, we created a calibrated WV-3 dataset with the corresponding ground truth. In addition, the suitability of visible/near-infrared (VNIR, 400–1040 nm) and short-wave infrared (SWIR, 1195 nm–2365 nm) bands of WV-3 to classification accuracy were examined and discussed in detail. To the best of our knowledge, this is the first effort to explore the significance of all SWIR bands in WV-3 for classification accuracy in a heterogeneous agricultural landscape. The results demonstrated that: (1) The proposed dPEN allows for building a deeper neural network from multispectral data which was the limitation of many convolutional neural networks; (2) dPEN was able to extract more discriminative features from VNIR and SWIR bands by producing the highest overall accuracy (OA: 86.06%) over competing methods such as support vector machine and random forest; (3) The inclusion of WV-3 SWIR bands greatly improved the classification accuracy; (4) SWIR bands were particularly beneficial to improve the classification accuracy of some individual classes such as weeds, crop residues, and corn and soybean during late developmental stages; (5) The red-edge band (705–745 nm) was identified as the most important band affecting the classification accuracy nearly 10%, whereas the coastal band (400–450 nm) provided the lowest contribution; and (6) SWIR-5 band (2145–2185 nm) contributed most to OA by enhancing it approximately 4% when combined with VNIR bands, while SWIR-1 (1195–1225 nm) yielded the lowest improvement (1.55%) for OA. These research outcomes provide useful information for efficiently mapping agricultural landscape, and indicate the potential practices of dPEN and contributions of spectral bands in WV-3 for plant phenotyping, weed control, and crop residue retention.
•A novel deep learning paradigm for landscape mapping using Worldview-3 data•A solution to develop a deeper neural network for multispectral data•Systematically analyze the significance of spectral bands of Worldview-3 data•SWIR bands are important in mapping of crop types, weeds, soil and residue.•The Red-edge band contributes most in mapping of crop types, foxtail, and soil.
PDF
