Fragile plant development gets severely hindered by harsh conditions under extremely high temperatures during prolonged dry spells. Drought impacts global food production severely in many areas. Disrupting physiological processes alters leaf gas exchange in profoundly negative ways, causing oxidative damage that really leads to lower yields. Plant sensitivity fluctuates wildly under drought conditions, owing somewhat mysteriously to factors like growth stage and genetic predisposition suddenly impacting yield
(Roychowdhury et al., 2023; Al-Sharqi et al., 2025). Legumes provide a remarkably sustainable source of protein, being vital in agriculture due to fixing atmospheric nitrogen effectively. Unique traits somehow enhance adaptability in environments severely lacking nitrogen. Drought severely impacts agricultural output due to its debilitating effects on crop development. Legume crops are frequently grown in rainfed areas and numerous Global Climate Models forecast a significant rise in drought frequency. Water scarcity at any stage may impede plant development, thus diminishing crop yield, particularly during grain filling and the reproductive period. The magnitude of yield reduction is contingent upon the severity and length of drought stress, the developmental stage of the crop and genotypic diversity. Consequently, the advancement of novel strategies to enhance drought resistance in legumes is essential for mitigating yield reductions in arid conditions.
Sophisticated methods boost legume yield under pretty tough conditions in arid regions somehow due to major advancements. Drought-resistant crops thrive with advanced breeding techniques and water-efficient methods including drip irrigation under harsh conditions. Drought stress severely impacts many facets of legume growth notably germination shoot development root development photosyn-thesis and reproductive processes
(Laranjeira et al., 2021). Climate change wreaks havoc extremely rapidly in certain regions becoming notoriously tough and restricting agricultural production severely it affects legume crops. Research indicates harsh drought severely impacts plant morphology physiology beneath extremely dry conditions but adequate moisture levels facilitate enzyme activation somehow. Stomatal regulation is essential in arid conditions, as it reduces water loss swiftly in an environment that is completely desiccated. Soybean stomatal conductance experiences a significant decline in response to drying effects that are overpowering in moderately stressful situations resulting from exceptionally severe drought conditions.
Drought severely impacts fertilization gametogenesis embryogenesis and grain development yielding pretty drastic outcomes. Blooming stages of plant life cycle become extremely fragile during severe droughts beneath murky waters causing pollen grain sterility and stunted floral development. Drought severely hampers plants’ ability generating additional blooms pods and seeds thus reducing overall yield substantially (
He et al., 2022). Drought has been seen to decrease pod quantity by 92.7% at the onset of pod development and by 81.6% throughout the pod elongation phase, relative to control conditions. The most significant decrease in seed quantity per plant transpired during the blooming phase. Nonetheless, dryness during the seed-setting phase decreased seed quantity and thus lowered final production. Recent research noted a substantial reduction in seed output in soybeans due to dry conditions.
To optimize agricultural yields in the face of plant difficulties and feed a large population sustainably, many ideas and methods outside disciplines must be used. A lot of work has to go into reducing yield losses; otherwise, things like availability and economic access, which are important for food security, would suffer. Even though biotic stressors like diseases get a lot of focus in plant stress research, pests still account for 20-30% of worldwide crop losses
(Rai et al., 2021). By identifying abiotic pressures, including water shortages under the soil’s surface, early on, substantial losses may be mitigated before they significantly impact yields. The agricultural industry is facing intensified challenges as a result of climate change, including drought stress and rising salt. Immediate enhancement of environmental monitoring is necessary to address these issues.
In this context, blockchain technology emerges as a transformative digital framework capable of ensuring secure, transparent and tamper-proof storage of agricultural data generated through sensor-based monitoring systems. By integrating blockchain with plant stress detection platforms, real-time drought data, spectral indices and physiological measurements can be immutably recorded, enabling traceable decision-making, improving trust among stakeholders and facilitating automated smart irrigation and crop management through decentralized architectures. This integration strengthens the reliability of drought assessment systems while enabling data sharing between researchers, farmers and policymakers without the risk of data manipulation or loss (
Cho, 2024;
Kim and AlZubi, 2024;
Min and Kim, 2024).
Blockchain-assisted agricultural monitoring systems also support precision farming by validating multisource sensor data and linking it with UAV-based imaging and machine learning analytics, thereby creating a robust ecosystem for early drought stress prediction, yield optimization and sustainable resource allocation
(Berger et al., 2022; Chen et al., 2025; Guebsi et al., 2024).
Direct detection approaches using bioreceptors for identifying plant diseases, such as “enzyme-linked immunosorbent assay (ELISA), flow cytometry (FC) and polymerase chain reaction (PCR)”, may be time-consuming, labor-intensive and technically challenging. Although expert eye observation is a potential bias method, it provides an additional tool for detecting biotic and abiotic stresses. Rapid disease detection, reliable results, as well as the capacity to perceive “biotic and abiotic stresses” are just a few of the many advantages that optical technologies provide over the previously described methods
(Muthuramalingam et al., 2022; Ma et al., 2024). Miniaturizing and improving the mobility of optical sensors is a key component of proximal sensing approaches. Regardless of these limitations, machine learning approaches may nevertheless handle complex data analysis and stress specificity by analyzing the available data for patterns related to the unique plant stress.
Plant tissues have numerous physiological characteristics affecting their light reflection capacity deeply beneath surface-level structures. Plant qualities fluctuate wildly under duress, modifying leaf reflectance spectrum in fairly complex ways. Chlorophyll, a pigment crucial for photosynthesis, may be influenced by variations in concentration due to environmental stress, leading to increased reflectance around 700 nm and reduced reflectance in 530-630 nm regions
(Ali et al., 2024). Pigments such as carotenes modify reflectance characteristics of a plant somehow beneath surface layers. Stress possibly alters leaf anatomical traits like epidermal cell convexity and cuticle thickness near elevated trichome density affecting spectral qualities. Exposure to UV radiation induces alterations in chlorophyll concentration it also increases leaf thickness thereby affecting chlorophyll fluorescence levels significantly. Reflectance within 950-970 nm spectrum gets affected by cell wall flexibility that dwindles rapidly under harsh drought conditions.
The biochemical components of leaves, can change under different conditions, affecting their reflectance characteristics. Salt stress can impair leaf mesophyll cells and modify the lignin content and polysaccharide of the cell wall. Optical sensors are used to assess plant health, with the sensor’s sensitivity to modified reflectance spectrum regions influenced by biotic and abiotic stressors determining its efficacy in stress detection
(Ojala et al., 2002). Hyperspectral imaging, which uses imaging and spectroscopic techniques, generates multi-dimensional data for the detection of plant phenotyping as well as stress in agriculture. It is commonly used to calculate vegetation indices (VIs) and spectral disease indices (SDIs) to differentiate plant diseases. The extensive spectrum data obtained by hyperspectral imaging has significant potential for creating novel Vegetation Indices (VIs) and Stress Detection Indices (SDIs) to identify specific plant stressors.
Hyperspectral imaging is a technology that uses spectroscopic methods to assess plant stress. It offers substantial data for analysis, yet it may be costly and cumbersome, hindering its applicability in real-time scenarios. Nonetheless, the advancement of portable spectroradiometers and compact hyperspectral cameras has resolved this challenge, enabling their implementation in real-time detection scenarios
(Liakos et al., 2018). Spectroradiometers cannot capture hyperspectral pictures but have been used in research to identify plant stressors. “Multispectral imaging and spectroscopy” uses data from a spectrum of wavelengths instead of several specific wavelengths or restricted bands. These approaches have effectively identified plant stressors, including graymold in tomato foliage, leaf spot disease in oilseed rape and nutritional shortages in tomato plants. Although multispectral methods provide more cost-effective sensors compared to hyperspectral ones, they provide less information on the plant and its surroundings owing to their larger wavebands. Nonetheless, they provide mobility and versatility, facilitating the creation of customized devices.
Thermal imaging and thermography are two techniques used for identifying plant stress. Thermal imaging quantifies outgoing radiation from an item, namely infrared radiation, to identify variations in surface temperature, which may indicate serious stress symptoms. Thermography is a straightforward technique that may be included in systems intended for the fast identification of plant stress
(Muchero et al., 2009). Nonetheless, it is significantly influenced by fluctuating climatic conditions, rendering it more suitable for precise setting applications than for open fields. Moreover, thermography exhibits a lack of specificity and offers a more generalized approach to detecting stress in plants. It is advisable to integrate “thermography” with other techniques for diagnosing certain disorders, since it cannot independently differentiate between various stressors and ailments.
Fluorescence spectroscopy is a technique used to measure the attenuation of incoming light by samples over various wavelengths. Fluorescent substances, like chlorophyll, absorb light at a designated wavelength and then release it at a certain, longer wavelength, facilitating the distinction between incoming and emitted light. “The primary forms of fluorescence released by vegetation are blue-green fluorescence (400-600 nm) and chlorophyll fluorescence (650-800 nm). Pulse-amplitude modulation (PAM) of the measuring light and continuous illumination are two primary techniques for obtaining fluorescence data in plants”. Chlorophyll fluorescence approaches include a reduced rate of photosynthesis due to stress and the consequent dissipation of chlorophyll fluorescence. Dark adaptation is essential for fluorescence kinetics experiments, enabling the assessment of the lowest fluorescence intensity. Plants are typically dark-adapted for 30 minutes prior to measurements being conducted.
Fluorescence proportions are used to assess data related to fluorescence for evaluating plant stressors. These ratios are beneficial for the detection of early stress. Fluorescence spectroscopy is a technique used to determine the position and quantity of certain constituents in plant samples using a narrow-band excitation light. It has been used in studies to identify “biotic and abiotic” challenges, like nutritional deficiencies in maize, drought stress in passion fruit, rapeseed, tomato and citrus canker in grapefruit trees
(Iuchi et al., 2000). However, fluorescence spectroscopy is deficient in specificity, as variations in fluorescence may signify multiple types of stressors. To discern specific pressures, it is essential to integrate this strategy with other approaches. Photoquenching or photobleaching is another problem associated with chlorophyll fluorescence kinetics, which can be mitigated by promptly evaluating fluorescence spectra after the introduction of excitation light.
Fluorescence imaging uses a camera to capture fluorescence pictures, yielding more information than individual spectra. It can differentiate the region of interest from non-interest, such as distinguishing crops from weeds. Fluorescence-based approaches have reasonable equipment costs, but they may not consistently provide definitive differentiation between healthy and sick plant tissues in the first stages of a disease. Other approaches may be required to enhance fluorescence for early disease identification.
Integrating several methodologies may provide enhanced insights into plant health, such as synthesizing data from many sensors. Although sensor integration has significant promise for generating precise and highly detailed data, further study is required to develop methodologies for amalgamating data from diverse sources with varying characteristics and managing the integrity, security and authenticity of such complex datasets, where blockchain-based distributed ledgers can serve as a secure backbone for multi-sensor data fusion, ensuring that spectral, thermal and fluorescence information remains verifiable and resistant to unauthorized alteration, thus reinforcing confidence in automated drought stress monitoring systems.
