A low-cost and open-source platform for automated imaging

Application of leaf multispectral analyzer in comparison to hyperspectral device to assess the diversity of spectral reflectance indices in wheat genotypes

Multispectral devices have a huge potential to be utilized in biological, ecological, and agricultural studies, providing valuable information on plant structure and chemical composition. The aim of the study was to assess the reliability and sensitivity of the affordable leaf spectrometer PolyPen (PP) in comparison with the highly sensitive analytical device FieldSpec-4. Measurements at the leaf level were realized on a collection of 24 diverse field-grown wheat (Triticum sp. L.) genotypes in several growth phases during the regular growing season, focusing on whole spectral curves and a set of 41 spectral reflectance indices. As expected, the sensitive analytical device showed a higher capacity to capture genotypic variability and the ability to distinguish seasonal changes compared to a low-cost multispectral device. Nevertheless, the analysis of the data provided by low-cost sensors provided a group of parameters with good sensitivity, including reasonable correlations between the records of the two devices (r > 0.80). Based on the large obtained datasets, we can conclude that the application of a low-cost PP leaf spectrometer in plant and crop studies can be efficient, but the selection of parameters is crucial. Thus, the present study provides valuable information for users of affordable leaf spectrometers in fundamental and applied plant science.

Prediction of Salicornia europaea L. biomass using a computer vision system to distinguish different salt-tolerant populations

BACKGROUND

Salicornia europaea L. is emerging as a versatile crop halophyte, requiring a low-cost, non-destructive method for salt tolerance classification to aid selective breeding. We propose using a computer vision system (CVS) with multivariate analysis to classify S. europaea based on morphometric and colour traits to predict plant biomass and the salinity in their substrate.

RESULTS

A trial and validation set of 96 and 24 plants from 2 populations confirmed the efficacy. CVS and multivariate analysis evaluated the plants by morphometric traits and CIELab colour variability. Through Pearson analysis, the strongest correlations were between biomass fresh weight (FW) vs. projected area (PA) (0.91) and anatomical cross-section (ACS) vs. shoot diameter (Sd) (0.94). The PA and FW correlation retrieved different equation fits between lower and higher salt-tolerant populations (R2 = 0.93 for linear and 0.90 for 2nd-degree polynomial), respectively. The higher salt-tolerant reached a maximum biomass PA at 400 mM NaCl, while the lower salt-tolerant produced less under 200 and 400 mM. A second Pearson correlation and PCA described sample variability with 80% reliability using only morphometric-colour parameters. Multivariate discriminant analysis (MDA) demonstrated that the method correctly classifies plants (90%) depending on their salinity level and tolerance, which was validated with 100% effectiveness. Through multiple linear regression, a predictive model successfully estimated biomass production by PA, and a second model predicted the salinity substrate (Sal.s.) where the plants thrive. Plants' Sd and height influenced PA prediction, while Sd and colour difference (ΔE1) influenced Sal.s. Models validation of actual vs. predicted values showed a R2 of 0.97 and 0.90 for PA, and 0.95 and 0.97 for Sal.s. for lower and higher salt-tolerant, respectively. This outcome confirms the method as a cost-effective tool for managing S. europaea breeding.

CONCLUSIONS

The CVS effectively extracted morphological and colour features from S. europaea cultivated at different salinity levels, enabling classification and plant sorting through image and multivariate analysis. Biomass and salinity substrate were accurately predicted by modelling non-destructive parameters. Enhanced by AI, machine learning and smartphone technology, this method shows great potential in ecology, bio-agriculture, and industry.

SPOT: Scanning plant IoT facility for high-throughput plant phenotyping

Many plant phenotyping platforms have been kept out of the reach of smaller labs and institutions due to high cost and proprietary software. The Scanning Plant IoT (SPOT) Facility, located at the University of Florida, is a mobile, laboratory-based platform that facilitates open-source collection of high-quality, interoperable plant phenotypic data. It consists of three main sensors: a hyperspectral sensor, a thermal camera, and a LiDAR camera. Real-time data from the sensors can be collected in its 10 ft. × 10 ft. scanning region. The mobility of the device allows its use in large growth chambers, environmentally controlled rooms, or greenhouses. Sensors are oriented nadir and positioned via computer numerical control of stepper motors. In a preliminary experiment, data gathered from SPOT was used to autonomously and nondestructively differentiate between cultivars.

Open-source workflow design and management software to interrogate duckweed growth conditions and stress responses

Duckweeds, a family of floating aquatic plants, are ideal model plants for laboratory experiments because they are small, easy to cultivate, and reproduce quickly. Duckweed cultivation, for the purposes of scientific research, requires that lineages are maintained as continuous populations of asexually propagating fronds, so research teams need to develop optimized cultivation conditions and coordinate maintenance tasks for duckweed stocks. Additionally, computational image analysis is proving to be a powerful duckweed research tool, but researchers lack software tools to assist with data collection and storage in a way that can feed into scripted data analysis. We set out to support these processes using a laboratory management software called Aquarium, an open-source application developed to manage laboratory inventory and plan experiments. We developed a suite of duckweed cultivation and experimentation operation types in Aquarium, which we then integrated with novel data analysis scripts. We then demonstrated the efficacy of our system with a series of image-based growth assays, and explored how our framework could be used to develop optimized cultivation protocols. We discuss the unexpected advantages and the limitations of this approach, suggesting areas for future software tool development. In its current state, our approach helps to bridge the gap between laboratory implementation and data analytical software for duckweed biologists and builds a foundation for future development of end-to-end computational tools in plant science.

A hyperspectral plant health monitoring system for space crop production

Compact and automated sensing systems are needed to monitor plant health for NASA's controlled-environment space crop production. A new hyperspectral system was designed for early detection of plant stresses using both reflectance and fluorescence imaging in visible and near-infrared (VNIR) wavelength range (400-1000 nm). The prototype system mainly includes two LED line lights providing VNIR broadband and UV-A (365 nm) light for reflectance and fluorescence measurement, respectively, a line-scan hyperspectral camera, and a linear motorized stage with a travel range of 80 cm. In an overhead sensor-to-sample arrangement, the stage translates the lights and camera over the plants to acquire reflectance and fluorescence images in sequence during one cycle of line-scan imaging. System software was developed using LabVIEW to realize hardware parameterization, data transfer, and automated imaging functions. The imaging unit was installed in a plant growth chamber at NASA Kennedy Space Center for health monitoring studies for pick-and-eat salad crops. A preliminary experiment was conducted to detect plant drought stress for twelve Dragoon lettuce samples, of which half were well-watered and half were under-watered while growing. A machine learning method using an optimized discriminant classifier based on VNIR reflectance spectra generated classification accuracies over 90% for the first four days of the stress treatment, showing great potential for early detection of the drought stress on lettuce leaves before any visible symptoms and size differences were evident. The system is promising to provide useful information for optimization of growth environment and early mitigation of stresses in space crop production.

Unmanned aerial vehicles for surveillance and control of vectors of malaria and other vector-borne diseases

The use of Unmanned Aerial Vehicles (UAVs) has expanded rapidly in ecological conservation and agriculture, with a growing literature describing their potential applications in global health efforts including vector control. Vector-borne diseases carry severe public health and economic impacts to over half of the global population yet conventional approaches to the surveillance and treatment of vector habitats is typically laborious and slow. The high mobility of UAVs allows them to reach remote areas that might otherwise be inaccessible to ground-based teams. Given the rapidly expanding examples of these tools in vector control programmes, there is a need to establish the current knowledge base of applications for UAVs in this context and assess the strengths and challenges compared to conventional methodologies. This review aims to summarize the currently available knowledge on the capabilities of UAVs in both malaria control and in vector control more broadly in cases where the technology could be readily adapted to malaria vectors. This review will cover the current use of UAVs in vector habitat surveillance and deployment of control payloads, in comparison with their existing conventional approaches. Finally, this review will highlight the logistical and regulatory challenges in scaling up the use of UAVs in malaria control programmes and highlight potential future developments.

A low-cost and open-source solution to automate imaging and analysis of cyst nematode infection assays for Arabidopsis thaliana

BACKGROUND

Cyst nematodes are one of the major groups of plant-parasitic nematode, responsible for considerable crop losses worldwide. Improving genetic resources, and therefore resistant cultivars, is an ongoing focus of many pest management strategies. One of the major bottlenecks in identifying the plant genes that impact the infection, and thus the yield, is phenotyping. The current available screening method is slow, has unidimensional quantification of infection limiting the range of scorable parameters, and does not account for phenotypic variation of the host. The ever-evolving field of computer vision may be the solution for both the above-mentioned issues. To utilise these tools, a specialised imaging platform is required to take consistent images of nematode infection in quick succession.

RESULTS

Here, we describe an open-source, easy to adopt, imaging hardware and trait analysis software method based on a pre-existing nematode infection screening method in axenic culture. A cost-effective, easy-to-build and -use, 3D-printed imaging device was developed to acquire images of the root system of Arabidopsis thaliana infected with the cyst nematode Heterodera schachtii, replacing costly microscopy equipment. Coupling the output of this device to simple analysis scripts allowed the measurement of some key traits such as nematode number and size from collected images, in a semi-automated manner. Additionally, we used this combined solution to quantify an additional trait, root area before infection, and showed both the confounding relationship of this trait on nematode infection and a method to account for it.

CONCLUSION

Taken together, this manuscript provides a low-cost and open-source method for nematode phenotyping that includes the biologically relevant nematode size as a scorable parameter, and a method to account for phenotypic variation of the host. Together these tools highlight great potential in aiding our understanding of nematode parasitism.

Weed Warden: A low-cost weed detection device implemented with spectral triad sensor for agricultural applications

Controlling weeds is essential for farmers to protect resources and maximize crop yield. Between crops, weeds are typically controlled by applying herbicides or tillage to the entire field. However, these control methods are expensive and can pose environmental risks. Robotic weeding systems are a good solution to minimize environmental impact and save money on herbicides, but they are expensive (>$100,000). The Weed Warden is a low-cost (<$200) plant detection sensor that can be mounted on rovers or tractors. The Weed Warden uses an open source multispectral sensor to detect live vegetation and sends a logic signal that could trigger a weed removal system such as a sprayer or mechanical tillage when vegetation is detected. We evaluate the Normalized Difference Vegetation Index (NDVI), Enhanced Normalized Difference Vegetation Index (ENDVI), and Enhanced Vegetation Index (EVI), for producing a value that, combined with a calibrated threshold, will indicate if there is plant life under the sensor. The Weed Warden system using ENDVI is most consistent at detection, with the ability to discriminate 7.6x7.6 cm vegetation samples from bare soil at sensor heights of 30 and 41 cm from the ground. The Weed Warden is a proof-of-concept component of an alternative system to robotic weeders of fallow fields that could help reduce costs, improve environmental outcomes in agricultural settings, and advance research into fallow field management practices.

MultipleXLab: A high-throughput portable live-imaging root phenotyping platform using deep learning and computer vision

BACKGROUND

Profiling the plant root architecture is vital for selecting resilient crops that can efficiently take up water and nutrients. The high-performance imaging tools available to study root-growth dynamics with the optimal resolution are costly and stationary. In addition, performing nondestructive high-throughput phenotyping to extract the structural and morphological features of roots remains challenging.

RESULTS

We developed the MultipleXLab: a modular, mobile, and cost-effective setup to tackle these limitations. The system can continuously monitor thousands of seeds from germination to root development based on a conventional camera attached to a motorized multiaxis-rotational stage and custom-built 3D-printed plate holder with integrated light-emitting diode lighting. We also developed an image segmentation model based on deep learning that allows the users to analyze the data automatically. We tested the MultipleXLab to monitor seed germination and root growth of Arabidopsis developmental, cell cycle, and auxin transport mutants non-invasively at high-throughput and showed that the system provides robust data and allows precise evaluation of germination index and hourly growth rate between mutants.

CONCLUSION

MultipleXLab provides a flexible and user-friendly root phenotyping platform that is an attractive mobile alternative to high-end imaging platforms and stationary growth chambers. It can be used in numerous applications by plant biologists, the seed industry, crop scientists, and breeding companies.

LED color gradient as a new screening tool for rapid phenotyping of plant responses to light quality

BACKGROUND

The increasing demand for local food production is fueling high interest in the development of controlled environment agriculture. In particular, LED technology brings energy-saving advantages together with the possibility of manipulating plant phenotypes through light quality control. However, optimizing light quality is required for each cultivated plant and specific purpose.

FINDINGS

This article shows that the combination of LED gradient set-ups with imaging-based non-destructive plant phenotyping constitutes an interesting new screening tool with the potential to improve speed, logistics, and information output. To validate this concept, an experiment was performed to evaluate the effects of a complete range of red:blue ratios on 7 plant species: Arabidopsis thaliana, Brachypodium distachyon, Euphorbia peplus, Ocimum basilicum, Oryza sativa, Solanum lycopersicum, and Setaria viridis. Plants were exposed during 30 days to the light gradient and showed significant, but species-dependent, responses in terms of dimension, shape, and color. A time-series analysis of phenotypic descriptors highlighted growth changes but also transient responses of plant shapes to the red:blue ratio.

CONCLUSION

This approach, which generated a large reusable dataset, can be adapted for addressing specific needs in crop production or fundamental questions in photobiology.

Image and fractal analysis as a tool for evaluating salinity growth response between two Salicornia europaea populations

BACKGROUND

This study describes a promising method for understanding how halophytes adapt to extreme saline conditions and to identify populations with greater resistance. Image and colour analyses have the ability to obtain many image parameters and to discriminate between different aspects in plants, which makes them a suitable tool in combination with genetic analysis to study the plants salt tolerance. To the best of our knowledge, there are no publications about the monitoring of halophytic plants by non-destructive methods for identifying the differences between plants that belong to different maternal salinity environments. The aim is to evaluate the ability of image analysis as a non-destructive method and principal component analysis (PCA) to identify the multiple responses of two S. europaea populations, and to determine which population is most affected by different salinity treatments as a preliminary model of selection.

RESULTS

Image analysis was beneficial for detecting the phenotypic variability of two S. europaea populations by morphometric and colour parameters, fractal dimension (FD), projected area (A), shoot height (H), number of branches (B), shoot diameter (S) and colour change (ΔE). S was found to strongly positively correlate with both proline content and ΔE, and negatively with chlorophyll content. These results suggest that proline and ΔE are strongly linked to plant succulence, while chlorophyll decreases with increased succulence. The negative correlation between FD and hydrogen peroxide (HP) suggests that when the plant is under salt stress, HP content increases in plants causing a reduction in plant complexity and foliage growth. The PCA results indicate that the greater the stress, the more marked the differences. At 400 mM a shorter distance between the factorial scores was observed. Genetic variability analysis provided evidence of the differences between these populations.

CONCLUSIONS

Our non-destructive method is beneficial for evaluating the halophyte development under salt stress. FD, S and ΔE were relevant indicators of plant architecture. PCA provided evidence that anthropogenic saline plants were more tolerant to saline stress. Furthermore, random amplified polymorphic DNA analysis provided a quick method for determining genetic variation patterns between the two populations and provided evidence of genetic differences between them.

Decoding Plant-Environment Interactions That Influence Crop Agronomic Traits

To ensure food security in the face of increasing global demand due to population growth and progressive urbanization, it will be crucial to integrate emerging technologies in multiple disciplines to accelerate overall throughput of gene discovery and crop breeding. Plant agronomic traits often appear during the plants' later growth stages due to the cumulative effects of their lifetime interactions with the environment. Therefore, decoding plant-environment interactions by elucidating plants' temporal physiological responses to environmental changes throughout their lifespans will facilitate the identification of genetic and environmental factors, timing and pathways that influence complex end-point agronomic traits, such as yield. Here, we discuss the expected role of the life-course approach to monitoring plant and crop health status in improving crop productivity by enhancing the understanding of plant-environment interactions. We review recent advances in analytical technologies for monitoring health status in plants based on multi-omics analyses and strategies for integrating heterogeneous datasets from multiple omics areas to identify informative factors associated with traits of interest. In addition, we showcase emerging phenomics techniques that enable the noninvasive and continuous monitoring of plant growth by various means, including three-dimensional phenotyping, plant root phenotyping, implantable/injectable sensors and affordable phenotyping devices. Finally, we present an integrated review of analytical technologies and applications for monitoring plant growth, developed across disciplines, such as plant science, data science and sensors and Internet-of-things technologies, to improve plant productivity.