Dhandapani Raju

Plants are continuously exposed to different environmental stresses that negatively impact their physiology and morphology, resulting in production reduction. As a result of constant pressure, plants evolve different mechanisms for sustenance and survival. Hormones play a major role in defences against the stresses and stimulate regulatory mechanisms. One of the ways through which they mitigate stress is via the production of hormones like auxins, ethylene, jasmonic acid, etc. The phytohormones help in signaling and enhance the chances of their survival. Plant hormones play many vital roles from integrating developmental events, physiological and biochemical processes to mediating both abiotic and biotic stresses. This book aims to highlight these issues and provide scope for the development of tolerance in crops against abiotic stresses to maximize yield for the growing population. There is an urgent need for the development of strategies, methods and tools for the broad-spectrum tolerance in plants supporting sustainable crop production under hostile environmental conditions. The salient features are as follows: • It includes both traditional and non-traditional phytohormones and focuses on the latest progress emphasizing the roles of different hormones under abiotic stresses. • It provides a scope of the best plausible and suitable options for overcoming these stresses and puts forward the methods for crop improvement. • It is an amalgamation of the biosynthesis of phytohormones and also provides molecular intricacies and signalling mechanisms in different abiotic stresses. • This book serves as a reference book for scientific investigators from recent graduates, academicians and researchers working on phytohormones and abiotic stresses.

The environment is in a phase of dynamism affecting and imparting stress to all the living organisms in an ecosystem. Stress is defined as any external factor that prevents plants from attaining their full ability to produce yield or genetic potential, thereby affecting their growth and development. Phytohormones play a crucial role in providing defence against environmental stress to plants. Strigolactones (SLs), one of the classes of plant hormones, are recently gaining attention due to their regulatory activities during abiotic stresses by functioning as both endogenous and exogenous signalling molecules. They are also found to be associated with root development, branching of shoots and reproduction. Strigolactones are a non-traditional hormone that is derived from the carotenoid pathway and are involved in growth and development, signalling and delaying senescence in plants. In this chapter, the different roles of strigolactones during abiotic stress in plants have been discussed, along with their applications in agriculture and crop improvement.

Oxylipins are a complex chemical class produced when plants oxidatively convert unsaturated fatty acids. They have a strong regulatory effect on growth, development and responses to environmental stimuli. Although a great deal of study has shown how important they are for protecting plants against biotic stressors, little is known about how important they are for adapting plants to abiotic stress situations. Oxylipins are involved in a variety of abiotic stress responses, including wounding, changes in light and temperature, dehydration, osmotic stress, exposure to ozone and heavy metal contamination. Based on strong data from genetic studies and biochemical dissections, we reveal that these oxygenated derivatives actively contribute to strengthening plant defensive mechanisms. Although the majority of the academic discussion has focused on oxylipins about biotic stress responses, this work shifts the emphasis to their critical functions in mitigating the complex effects of abiotic stressors. According to recent research, there may be a way to use plant oxylipins as biocontrol agents, which could lead to new opportunities for improving crop resilience and environmental sustainability. This chapter provides an overview highlighting the significance of oxylipin-mediated abiotic stress responses in plants, as well as providing insight into the processes behind these responses.

Phytohormones are essential, small signalling molecules that have a significant impact on plant development and yield output. Phytohormones (PHs) play a critical role in the modulation of numerous physiological and biochemical processes that control plant development and production under both ideal and stressful circumstances. Hormonal cross-regulation sets off a chain of events that precisely adjusts physiological processes in plant design, enabling plants to develop in less-than-ideal circumstances. Plant hormones generally can be divided into two groups based on their metabolism. Plant growth-promoting processes like cell division, cell lengthening, seed and fruit development and differentiation patterns come under the purview of class one phytohormones. The reaction of plants to biotic and abiotic stressors is influenced by the second class of hormones. In this chapter, the timeline of phytohormone research and development will be described briefly. It will also deal with the development of phytohormones in the past and present, how they are developing and how the progress in future research mainly concentrates on areas around abiotic stress.

Hormones are chemical messengers that coordinate the cell functions of multicellular organisms. Animals produce many chemical hormones, each of which generally targets a few cells and triggers a specific response. However, plant hormones (phytohormones) are less numerous, usually affect most, if not all cells, and trigger various responses. Moreover, the accumulation and effects of each phytohormone are modulated by the influences of the environment and development as well as by the activities of other phytohormones. Unraveling complex hormonal action networks and signaling pathways in plants is underway; several hormonal receptors have only recently been identified, and many signaling components are still unidentified. This chapter gives a brief introduction to plant hormones, their functions throughout the life cycle of plants, their cell effects, signaling pathways, as well as their interactions with other hormones.

Developing crop plants with improved thermotolerance mechanisms can overcome the adverse abiotic stress on the growth and development of plants. Approaches taken to develop stress-tolerant plants include breeding, genetic engineering and the use of growth regulators. A better understanding of plants' physiological, biochemical and molecular responses in response to abiotic stress is needed for the development of stress tolerance in crop plants. In natural genotypes, substantial variation is seen for stress tolerance. Each genotype responds differently under stress in terms of bringing changes in stress-related physiological traits. Increasing evidence suggests that plants use endogenous hormones to couple their growth rate to environmental conditions like changing temperature rather than subject to a passive inhibition of growth. Plant hormones are an essential player in signal cascade and are involved in induction of plant stress responses as plant hormones play roles in the deduction, transduction and induction phase of the stress response. Crosstalk in hormone signaling reflects an organism's ability to integrate different inputs and respond appropriately. Hormonal homeostasis, stability, content, biosynthesis and compartmentalization are altered under heat stress. Here, the chapter describes the roles of cytokinins under abiotic stress tolerance.

The advent of high-spatial-resolution hyperspectral imagery from unmanned aerial vehicles (UAVs) made a breakthrough in the detailed retrieval of crop traits for precision crop-growth monitoring systems. Here, a hybrid approach of radiative transfer modelling combined with a machine learning (ML) algorithm is proposed for the retrieval of the leaf area index (LAI) and canopy chlorophyll content (CCC) of wheat cropland at the experimental farms of ICAR-Indian Agricultural Research Institute (IARI), New Delhi, India. A hyperspectral image captured from a UAV platform with spatial resolution of 4 cm and 269 spectral bands ranging from 400 to 1000 nm was processed for the retrieval of the LAI and CCC of wheat cropland. The radiative transfer model PROSAIL was used for simulating spectral data, and eight machine learning algorithms were evaluated for hybrid model development. The ML Gaussian process regression (GPR) algorithm was selected for the retrieval of crop traits due to its superior accuracy and lower associated uncertainty. Simulated spectra were sampled for training GPR models for LAI and CCC retrieval using dimensionality reduction and active learning techniques. LAI and CCC biophysical maps were generated from pre-processed hyperspectral data using trained GPR models and validated against in situ measurements, yielding R² values of 0.889 and 0.656, suggesting high retrieval accuracy. The normalised root mean square error (NRMSE) values reported for LAI and CCC retrieval are 8.579% and 14.842%, respectively. The study concludes with the development of optimized GPR models tailored for UAV-borne hyperspectral data for the near-real-time retrieval of wheat traits. This workflow can be upscaled to farmers’ fields, facilitating efficient crop monitoring and management.

Among seed attributes, weight is one of the main factors determining the soybean harvest index. Recently, the focus of soybean breeding has shifted to improving seed size and weight for crop optimization in terms of seed and oil yield. With recent technological advancements, there is an increasing application of imaging sensors that provide simple, real-time, non-destructive, and inexpensive image data for rapid image-based prediction of seed traits in plant breeding programs. The present work is related to digital image analysis of seed traits for the prediction of hundred-seed weight (HSW) in soybean. The image-based seed architectural traits (i-traits) measured were area size (AS), perimeter length (PL), length (L), width (W), length-to-width ratio (LWR), intersection of length and width (IS), seed circularity (CS), and distance between IS and CG (DS). The phenotypic investigation revealed significant genetic variability among 164 soybean genotypes for both i-traits and manually measured seed weight. Seven popular machine learning (ML) algorithms, namely Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Random Forest (RF), Support Vector Regression (SVR), LASSO Regression (LR), Ridge Regression (RR), and Elastic Net Regression (EN), were used to create models that can predict the weight of soybean seeds based on the image-based novel features derived from the Red-Green-Blue (RGB)/visual image. Among the models, random forest and multiple linear regression models that use multiple explanatory variables related to seed size traits (AS, L, W, and DS) were identified as the best models for predicting seed weight with the highest prediction accuracy (coefficient of determination, R²⁼0.98 and 0.94, respectively) and the lowest prediction error, i.e., root mean square error (RMSE) and mean absolute error (MAE). Finally, principal components analysis (PCA) and a hierarchical clustering approach were used to identify IC538070 as a superior genotype with a larger seed size and weight. The identified donors/traits can potentially be used in soybean improvement programs

Phenomics technologies have advanced rapidly in the recent past for precision phenotyping of diverse crop plants. High-throughput phenotyping using imaging sensors has been proven to fetch more informative data from a large population of genotypes than the traditional destructive phenotyping methodologies. It provides accurate, high-dimensional phenome-wide big data at an ultra-super spatial and temporal resolution. Biomass is an important plant phenotypic trait that can reflect the agronomic performance of crop plants in terms of growth and yield. Several image-derived features such as area, projected shoot area, projected shoot area with height constant, estimated bio-volume, etc., and machine learning models (single or multivariate analysis) are reported in the literature for use in the non-invasive prediction of biomass in diverse crop plants. However, no studies have reported the best suitable image-derived features for accurate biomass prediction, particularly for fully grown rice plants (70DAS). In this present study, we analyzed a subset of rice recombinant inbred lines (RILs) which were developed from a cross between rice varieties BVD109 × IR20 and grown in sufficient (control) and deficient soil nitrogen (N stress) conditions. Images of plants were acquired using three different sensors (RGB, IR, and NIR) just before destructive plant sampling for the quantitative estimation of fresh (FW) and dry weight (DW). A total of 67 image-derived traits were extracted and classified into four groups, viz., geometric-, color-, IR- and NIR-related traits. We identified a multimodal trait feature, the ratio of PSA and NIR grey intensity as estimated from RGB and NIR sensors, as a novel trait for predicting biomass in rice. Among the 16 machine learning models tested for predicting biomass, the Bayesian regularized neural network (BRNN) model showed the maximum predictive power (R² = 0.96 and 0.95 for FW and DW of biomass, respectively) with the lowest prediction error (RMSE and bias value) in both control and N stress environments. Thus, biomass can be accurately predicted by measuring novel image-based parameters and neural network-based machine learning models in rice.