A Review of Remote Sensing Challenges for Food Security with Respect to Salinity and Drought Threats

Variability in morpho-biochemical, photosynthetic pigmentation, enzymatic and quality attributes of potato for salinity stress tolerance

Salt stress has emerged as a growing global concern, exerting a significant impact on agricultural productivity. The challenges of salt stress on potatoes are crucial for ensuring food security and sustainable agriculture. To address this issue a pot trial was executed to evaluate the impacts of NaCl in the soil on the growth, photosynthetic pigments, and quality attributes of potato, plants were grown in soil spiked with various concentrations of NaCl (0, 1, 3, 5, 7 g kg-1 of soil). Results revealed that salt stress have negative impacts on the growth, biomass, photosynthesis and quality attributes of potato. Lower level of salt stress 1 g kg-1 of soil improved the fresh and dry biomass of leaves (78.70 and 47.74%) and tubers (86.04 and 88.92%) as compared to control, respectively. Higher levels of salt stress (7 g kg-1) increased lipid peroxidation in leaves and improved the enzymatic antioxidants. It was observed that enzyme activities i.e., SOD (134.97%), POD (101.02%), and CAT (28.87%) increased in leaves and are inversely related to the NaCl concentration. The combination of reduction in chlorophyll contents and soluble sugars resulted in lower levels of quality attributes i.e., amylose (68.90%) and amylopectin (16.70%) of potato. Linear relationship in growth, biomass and physiological attributes showed the strong association with increased salt stress. Furthermore, the PCA-heatmap synergy offers identifying clusters of co-regulated attributes, which pinpoint the physiological responses that exhibit the strongest correlation with increasing salt stress levels. Findings indicate that potato can be grown successfully with (1 g kg-1 of NaCl in soil) without negative impacts on plant quality. Furthermore, this study contributes valuable insights into the complexities of salt stress on potato plants and provides a foundation for developing strategies to enhance their resilience in salt-affected environments.

Forest fuel type classification: Review of remote sensing techniques, constraints and future trends

Improved forest management plans require a better understanding of wildfire risk and behavior to enhance the conservation of biodiversity and plan effective risk mitigation activities across the landscape. More particularly, for spatial fire hazard and risk assessing as well as fire intensity and growth modeling across a landscape, an adequate knowledge of the spatial distribution of key forest fuels attributes is required. Mapping fuel attributes is a challenging and complicated procedure because fuels are highly variable and complex. To simplify, classification schemes are used to summarize the large number of fuel attributes (e.g., height, density, continuity, arrangement, size, form, etc.) into fuel types which groups vegetation classes with a similar predicted fire behavior. Remote sensing is a cost-effective and objective technology that have been used to regularly map fuel types and have demonstrated greater success compared to traditional field surveys, especially with recent advancements in remote sensing data acquisition and fusion techniques. Thus, the main goal of this manuscript is to provide a comprehensive review of the recent remote sensing approaches used for fuel type classification. We build on findings from previous review manuscripts and focus on identifying the key challenges of different mapping approaches and the research gaps that still need to be filled in. To improve classification outcomes, more research into developing state-of-the-art deep learning algorithms with integrated remote sensing data sources is encouraged for future research. This review can be used as a guideline for practitioners, researchers, and decision-makers in the domain of fire management service.

An integrated approach of remote sensing and geospatial analysis for modeling and predicting the impacts of climate change on food security

The agriculture sector provides the majority of food supplies, ensures food security, and promotes sustainable development. Due to recent climate changes as well as trends in human population growth and environmental degradation, the need for timely agricultural information continues to rise. This study analyzes and predicts the impacts of climate change on food security (FS). For 2002-2021, Landsat, MODIS satellite images and predisposing variables (land surface temperature (LST), evapotranspiration, precipitation, sunny days, cloud ratio, soil salinity, soil moisture, groundwater quality, soil types, digital elevation model, slope, and aspect) were used. First, we used a deep learning convolutional neural network (DL-CNN) based on the Google Earth Engine (GEE) to detect agricultural land (AL). A remote sensing-based approach combined with the analytical network process (ANP) model was used to identify frost-affected areas. We then analyzed the relationship between climatic, geospatial, and topographical variables and AL and frost-affected areas. We found negative correlations of - 0.80, - 0.58, - 0.43, and - 0.45 between AL and LST, evapotranspiration, cloud ratio, and soil salinity, respectively. There is a positive correlation between AL and precipitation, sunny days, soil moisture, and groundwater quality of 0.39, 0.25, 0.21, and 0.77, respectively. The correlation between frost-affected areas and LST, evapotranspiration, cloud ratio, elevation, slope, and aspect are 0.55, 0.40, 0.52, 0.35, 0.45, and 0.39. Frost-affected areas have negative correlations with precipitation, sunny day, and soil moisture of - 0.68, - 0.23, and - 0.38, respectively. Our findings show that the increase in LST, evapotranspiration, cloud ratio, and soil salinity is associated with the decrease in AL. Additionally, AL decreases with a decreasing in precipitation, sunny days, soil moisture, and groundwater quality. It was also found that as LST, evapotranspiration, cloud ratio, elevation, slope, and aspect increase, frost-affected areas increase as well. Furthermore, frost-affected areas increase when precipitation, sunny days, and soil moisture decrease. Finally, we predicted the FS threat for 2030, 2040, 2050, and 2060 using the CA-Markov method. According to the results, the AL will decrease by 0.36% from 2030 to 2060. Between 2030 and 2060, however, the area with very high frost-affected will increase by about 10.64%. In sum, this study accentuates the critical impacts of climate change on the FS in the region. Our findings and proposed methods could be helpful for researchers to model and quantify the climate change impacts on the FS in different regions and periods.

Probabilistic forecasting of remotely sensed cropland vegetation health and its relevance for food security

In a world where climate change, population growth, and global diseases threaten economic access to food, policies and contingency plans can strongly benefit from reliable forecasts of agricultural vegetation health. To inform decisions, it is also crucial to quantify the forecasting uncertainty and prove its relevance for food security. Yet, in previous studies both these aspects have been largely overlooked. This paper develops a methodology to anticipate the agricultural Vegetation Health Index (VHI) while making the underlying prediction uncertainty explicit. To achieve this aim, a probabilistic machine learning framework modelling weather and climate determinants is introduced and implemented through Quantile Random Forests. In a second step, a statistical link between VHI forecasts and monthly food price variations is established. As a pilot implementation, the framework is applied to nine countries of South-East Asia (SEA) with consideration of national monthly rice prices. Model benchmarks show satisfactory accuracy metrics, suggesting that the probabilistic VHI predictions can provide decision-makers with reliable information about future cropland health and its impact on food price variation weeks or even months ahead, albeit with increasing uncertainty as the forecasting horizon grows. These results - ultimately allowing to anticipate the impact of weather shocks on household food expenditure - contribute to advancing the multidisciplinary literature linking vegetation health, probabilistic forecasting models, and food security policy.

Nitrogen form differently modulates growth, metabolite profile, and antioxidant and nitrogen metabolism activities in roots of Spartina alterniflora in response to increasing salinity

Sodium tolerance and nitrogen-source preferences are two of the most fascinating and ecologically important areas in plant physiology. Spartina alterniflora is a highly salt-tolerant species and appears to prefer ammonium (NH₄⁺) over nitrate (NO₃^-) as an inorganic N source, presenting a suite of aboveground physiological and biochemical mechanisms that allows growth in saline environments. Here, we tested the interactive effects of salinity (0, 200, 500 mM NaCl) and nitrogen source (NO₃^-, NH₄⁺, NH₄NO₃) on some physiological and biochemical parameters of S. alterniflora at the root level. After three months of treatments, plants were harvested to determine root growth parameters and total amino acids, proline, total soluble sugars, sucrose, and root enzyme activity. The control (0 mM NaCl) had the highest root growth rate in the medium containing only ammonium and the lowest in the medium containing only nitrate. Except for NO₃^--fed plants, the 200 mM NaCl treatment generally had less root growth than the control. Under high salinity, NH₄⁺-fed plants had better root growth than NO₃^--fed plants. In the absence of salinity, NH₄⁺-fed plants had higher superoxide dismutase, ascorbate peroxidase, glutathione reductase, and guaiacol peroxidase activities than NO₃^--fed plants. Salinity generally promoted the activity of the principal antioxidant enzymes, more so in NH₄⁺-fed plants. Nitrogen metabolism was characterized by higher constitutive levels of glutamate dehydrogenase (GDH) activity under ammonia nutrition, accompanied by elevated total amino acids levels in roots. The advantage of ammonium nutrition for S. alterniflora under salinity was connected to high amino acid accumulation and antioxidant enzyme activities, together with low H₂O₂ concentration and increased GDH activity. Ammonium improved root performance of S. alterniflora, especially under saline conditions, and may improve root antioxidant capacity and N-assimilating enzyme activities, and adjust osmotically to salinity by accumulating amino acids.

Toward Automated Machine Learning-Based Hyperspectral Image Analysis in Crop Yield and Biomass Estimation

The incorporation of autonomous computation and artificial intelligence (AI) technologies into smart agriculture concepts is becoming an expected scientific procedure. The airborne hyperspectral system with its vast area coverage, high spectral resolution, and varied narrow-band selection is an excellent tool for crop physiological characteristics and yield prediction. However, the extensive and redundant three-dimensional (3D) cube data processing and computation have made the popularization of this tool a challenging task. This research integrated two important open-sourced systems (R and Python) combined with automated hyperspectral narrowband vegetation index calculation and the state-of-the-art AI-based automated machine learning (AutoML) technology to estimate yield and biomass, based on three crop categories (spring wheat, pea and oat mixture, and spring barley with red clover) with multifunctional cultivation practices in northern Europe and Estonia. Our study showed the estimated capacity of the empirical AutoML regression model was significant. The best coefficient of determination (R2) and normalized root mean square error (NRMSE) for single variety planting wheat were 0.96 and 0.12 respectively; for mixed peas and oats, they were 0.76 and 0.18 in the booting to heading stage, while for mixed legumes and spring barley, they were 0.88 and 0.16 in the reproductive growth stages. In terms of straw mass estimation, R2 was 0.96, 0.83, and 0.86, and NRMSE was 0.12, 0.24, and 0.33 respectively. This research contributes to, and confirms, the use of the AutoML framework in hyperspectral image analysis to increase implementation flexibility and reduce learning costs under a variety of agricultural resource conditions. It delivers expert yield and straw mass valuation two months in advance before harvest time for decision-makers. This study also highlights that the hyperspectral system provides economic and environmental benefits and will play a critical role in the construction of sustainable and intelligent agriculture techniques in the upcoming years.

Year-Independent Prediction of Food Insecurity Using Classical and Neural Network Machine Learning Methods

Current food crisis predictions are developed by the Famine Early Warning System Network, but they fail to classify the majority of food crisis outbreaks with model metrics of recall (0.23), precision (0.42), and f1 (0.30). In this work, using a World Bank dataset, classical and neural network (NN) machine learning algorithms were developed to predict food crises in 21 countries. The best classical logistic regression algorithm achieved a high level of significance (p < 0.001) and precision (0.75) but was deficient in recall (0.20) and f1 (0.32). Of particular interest, the classical algorithm indicated that the vegetation index and the food price index were both positively correlated with food crises. A novel method for performing an iterative multidimensional hyperparameter search is presented, which resulted in significantly improved performance when applied to this dataset. Four iterations were conducted, which resulted in excellent 0.96 for metrics of precision, recall, and f1. Due to this strong performance, the food crisis year was removed from the dataset to prevent immediate extrapolation when used on future data, and the modeling process was repeated. The best “no year” model metrics remained strong, achieving ≥0.92 for recall, precision, and f1 while meeting a 10% f1 overfitting threshold on the test (0.84) and holdout (0.83) datasets. The year-agnostic neural network model represents a novel approach to classify food crises and outperforms current food crisis prediction efforts.

Bacterial Plant Biostimulants: A Sustainable Way towards Improving Growth, Productivity, and Health of Crops

This review presents a comprehensive and systematic study of the field of bacterial plant biostimulants and considers the fundamental and innovative principles underlying this technology. Plant biostimulants are an important tool for modern agriculture as part of an integrated crop management (ICM) system, helping make agriculture more sustainable and resilient. Plant biostimulants contain substance(s) and/or microorganisms whose function when applied to plants or the rhizosphere is to stimulate natural processes to enhance plant nutrient uptake, nutrient use efficiency, tolerance to abiotic stress, biocontrol, and crop quality. The use of plant biostimulants has gained substantial and significant heed worldwide as an environmentally friendly alternative to sustainable agricultural production. At present, there is an increasing curiosity in industry and researchers about microbial biostimulants, especially bacterial plant biostimulants (BPBs), to improve crop growth and productivity. The BPBs that are based on PGPR (plant growth-promoting rhizobacteria) play plausible roles to promote/stimulate crop plant growth through several mechanisms that include (i) nutrient acquisition by nitrogen (N2) fixation and solubilization of insoluble minerals (P, K, Zn), organic acids and siderophores; (ii) antimicrobial metabolites and various lytic enzymes; (iii) the action of growth regulators and stress-responsive/induced phytohormones; (iv) ameliorating abiotic stress such as drought, high soil salinity, extreme temperatures, oxidative stress, and heavy metals by using different modes of action; and (v) plant defense induction modes. Presented here is a brief review emphasizing the applicability of BPBs as an innovative exertion to fulfill the current food crisis.