Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods.

Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants. This study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages. To achieve this, a nutrient experiment with four treatments (high and low levels of nitrogen and phosphorus) was conducted in a glasshouse. A hybrid CNN model comprising a 3D CNN (extracts joint spectral-spatial information) and a 2D CNN (for abstract spatial information extraction) was proposed. Three pre-processing techniques, including second-order derivative, standard normal variate, and linear discriminant analysis, were applied to selected regions of interest within the plant spectral hypercube. Together with the raw data, these datasets were used as inputs to train the proposed model. This was done to assess the impact of different pre-processing techniques on hyperspectral-based nutrient phenotyping. The performance of the proposed model was compared with a 3D CNN, a 2D CNN, and a Hybrid Spectral Network (HybridSN) model. Effective wavebands were selected from the best-performing dataset using a greedy stepwise-based correlation feature selection (CFS) technique. The selected wavebands were then used to retrain the models to identify the nutrient status at five selected plant growth stages. From the results, the proposed hybrid model achieved a classification accuracy of over 94% on the test dataset, demonstrating its potential for identifying nitrogen and phosphorus status in cowpea and quinoa at different growth stages.

Field phenotyping for African crops: overview and perspectives.

Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers for the African plant science community to measure complex quantitative phenotypes and to establish the genetic basis of agriculturally relevant traits. These advances will facilitate the screening of germplasm for optimum performance and adaptation to low-input agriculture and resource-constrained environments. Increasing the capacity to investigate plant function and structure through non-invasive technologies is an effective strategy to aid plant breeding and additionally may contribute to precision agriculture. However, despite the significant global advances in basic knowledge and sensor technology for plant phenotyping, Africa still lags behind in the development and implementation of these systems due to several practical, financial, geographical and political barriers. Currently, field phenotyping is mostly carried out by manual methods that are prone to error, costly, labor-intensive and may come with adverse economic implications. Therefore, improvements in advanced field phenotyping capabilities and appropriate implementation are key factors for success in modern breeding and agricultural monitoring. In this review, we provide an overview of the current state of field phenotyping and the challenges limiting its implementation in some African countries. We suggest that the lack of appropriate field phenotyping infrastructures is impeding the development of improved crop cultivars and will have a detrimental impact on the agricultural sector and on food security. We highlight the prospects for integrating emerging and advanced low-cost phenotyping technologies into breeding protocols and characterizing crop responses to environmental challenges in field experimentation. Finally, we explore strategies for overcoming the barriers and maximizing the full potential of emerging field phenotyping technologies in African agriculture. This review paper will open new windows and provide new perspectives for breeders and the entire plant science community in Africa.

Machine Learning Methods for Automatic Segmentation of Images of Field- and Glasshouse-Based Plants for High-Throughput Phenotyping.

Image segmentation is a fundamental but critical step for achieving automated high- throughput phenotyping. While conventional segmentation methods perform well in homogenous environments, the performance decreases when used in more complex environments. This study aimed to develop a fast and robust neural-network-based segmentation tool to phenotype plants in both field and glasshouse environments in a high-throughput manner. Digital images of cowpea (from glasshouse) and wheat (from field) with different nutrient supplies across their full growth cycle were acquired. Image patches from 20 randomly selected images from the acquired dataset were transformed from their original RGB format to multiple color spaces. The pixels in the patches were annotated as foreground and background with a pixel having a feature vector of 24 color properties. A feature selection technique was applied to choose the sensitive features, which were used to train a multilayer perceptron network (MLP) and two other traditional machine learning models: support vector machines (SVMs) and random forest (RF). The performance of these models, together with two standard color-index segmentation techniques (excess green (ExG) and excess green-red (ExGR)), was compared. The proposed method outperformed the other methods in producing quality segmented images with over 98%-pixel classification accuracy. Regression models developed from the different segmentation methods to predict Soil Plant Analysis Development (SPAD) values of cowpea and wheat showed that images from the proposed MLP method produced models with high predictive power and accuracy comparably. This method will be an essential tool for the development of a data analysis pipeline for high-throughput plant phenotyping. The proposed technique is capable of learning from different environmental conditions, with a high level of robustness.

Abscisic acid-responsive element binding transcription factors contribute to proline synthesis and stress adaptation in Arabidopsis.

Proline accumulation is one of the most common adaptive responses of higher plants against abiotic stresses like drought. It plays multiple roles in osmotic adjustment, cell homeostasis and stress recovery. Genetic regulation of proline accumulation under drought is complex, and transcriptional cascades modulating proline is poorly understood. Here, we employed quadruple mutant (abf1 abf2 abf3 abf4) to dissect the role of ABA-responsive elements (ABREs) binding transcription factors (ABFs) in modulating proline accumulation across varying stress scenarios. ABREs are present across the promoter of the P5CS1 gene, whose upregulation is considered a hallmark for drought inducible proline accumulation. Upon ABA treatment, P5CS1 mRNA expression and proline content in the shoot were significantly higher in Col-0 compared to the quadruple mutant. Similar results were found at 2 h and 3 h after acute dehydration. We quantified proline at different time points after drought stress treatment. The proline content was higher in wild type (Col-0) than the quadruple mutant at the early stage of drought. Notably, the proline accumulation in wild type increased at a slower rate than the quadruple mutant 7 d after drought stress. Besides, the quadruple mutant displayed significant oxidative damage, low tissue turgidity and higher membrane damage under terminal drought stress. Both terminal drought stress and long-term constant water stress revealed substantial differences in growth rate between wild type and quadruple mutant. The study provides evidence that ABFs are involved in drought stress response, such as proline biosynthesis in Arabidopsis.

Multicomponent Polymerization toward Cationic Polymers for Efficient Gene Delivery.

A new class of cationic polymers containing tertiary amine, thioether, and hydroxyl groups are prepared via a catalyst-free, multicomponent polymerization method using dithiol, formaldehyde, and di-sec-amine with a ratio of 1:2:1, to access a library of water-soluble polymers with well-defined structures and suitable molecular weights (Mw ranging from 5000 to 8000 Da) in high yields (up to 90%). Such polycations are demonstrated to be promising nonviral gene delivery vectors with high transfection efficiency (up to 3.5-fold of PEI25k) and low toxicity with multiple functionalities: 1) efficient gene condensation by tertiary amine groups; 2) reactive oxygen species scavenging by thioether groups; and 3) positive charge shielding by hydroxyl groups. Both the thioether and hydroxyl groups are contributed to reduce the cytotoxicity of the polycations by tuning the oxidative stress and preventing the undesired serum binding. The optimized polycations can achieve high transfection efficiency under the serum conditions, indicating the great potential as a nonviral gene delivery vector candidate for clinical application.