Mountainous regions are more sensitive to climatic condition changes and are susceptible to recent increases in temperature. Due to urbanization and land use/land cover (LULC) issues, Cameron Highlands has been impacted by rising land surface temperature (LST) variation. Thus, this study was carried out to explore the impact of the LULC change on LST in the Cameron Highlands from 2009 to 2019 using remote sensing images acquired from Landsat 7 ETM+, Landsat 8 Operational Land Imager (OLI/TIRS), and Moderate Resolution Imaging Spectroradiometer (MODIS) 11A Thermal sensors. A split-window algorithm was applied to Landsat 8 images (2013–2019) to derive the LST. Air temperature data of the study area were also obtained to cross-validate data sources. Based on the validation results, the accuracy of LULC and LST outputs were more than 94.6% and 80.0%, respectively. The results show that the current trend of urban growth continues at a rate of 0.16% per year, and the area experienced an LST increase of 2 °C between 2009 and 2019. This study is crucial for land planners and environmentalists to understand the impacts of LULC change on LST and to propose appropriate policy measures to control development in Cameron Highlands.

The influence of regional climate change (CC) on agricultural production variance in the Mediterranean region has been discussed based on the assessment of the last decade. Most of the Mediterranean region has experienced frequent natural disasters, expanding population, increase in temperature, and increase in the surface of the Mediterranean Sea. Furthermore, the temperature in the Mediterranean area is rising 25% faster than the rest of the globe, and in the summer, it is warming 40% faster than the global average. Climate change can alter the food supply, restrict access to food, and degrade food quality. Temperature rises, changes in precipitation patterns, changes in severe weather events, and decreased water availability, for example, might all result in lower agricultural production. The fact that most Mediterranean nations rely on imported basic foodstuffs adds to the severity of the situation. Instability and insecurity of agricultural supply in the region might lead to massive population movement, transforming most Mediterranean nations into a global source of instability. Based on the experience of similar geographical locations, the article has highlighted the essential elements affecting crop productivity and the five domains of water, ecosystems, food, health, and security. Despite the region’s complexity, the Mediterranean region has been offered an overall assessment that predicts the best strategy for the best solution. Such an attempt describes a methodical integration of scientific discoveries to understand better the combined hazards illustrated by the fact that CC has affected food production, resulting in widespread insecurity. Utilizing current technologies in agricultural production has been recommended to support regional nations in reaching higher yields. The significance of this study could be realized by mitigating climatic shocks through a sustainable food production system to accomplish development goals in vulnerable nations.

The betaine aldehyde dehydrogenase (BADH) gene plays a multifunctional role in plants. It is an important factor in fragrance production, abiotic stresses and antibiotic-free selection of transgenic plants. Molecular studies have presented a new picture of this critical factor involved in abiotic stress responses via the MAPK (mitogen-activated protein kinase) signalling pathway in numerous plants. Besides BADH, glycine betaine performs an important function in plant tolerance to environmental stresses. The presence of glycine betaine can help maintain the integrity of cell membranes against unexpected environmental stresses. BADH leads to production of glycine betaine through the oxidation of betaine aldehyde. Hence, BADH is considered a key regulator for glycine betaine formation. Consequently, by providing glycine betaine as a chemical interface, there is a critical role of BADH in enhancing the tolerance in an extensive range of plants subjected to different destructive abiotic stresses. The present article reviews the significant multifunctional role of the BADH gene in various plants, and also particularly argues how this important gene plays a responsive function to different destructive abiotic stresses, and its potential use in crop improvement using advanced technologies. Consequently, cloning of more BADH genes, specially from stress-tolerant plants, discovering their responsive signalling roles under environmental stresses, and validating such candidates for their potential are very helpful, and can open new windows to generate new stress-resistant crop cultivars.

Basal Stem Rot (BSR), a disease caused by Ganoderma boninense (G. boninense), has posed a significant concern for the oil palm industry, particularly in Southeast Asia, as it has the potential to cause substantial economic losses. The breeding programme is currently searching for G. boninense-resistant planting materials, which has necessitated intense manual screening in the nursery to track the progression of disease development in response to different treatments. The combination of hyperspectral image and machine learning approaches has a high detection potential for BSR. However, manual feature selection is still required to construct a detection model. Therefore, the objective of this study is to establish an automatic BSR detection at the seedling stage using a pre-trained deep learning model and hyperspectral images. The aerial view image of an oil palm seedling is divided into three regions in order to determine if there is any substantial spectral change across leaf positions. To investigate if the background images affect the performance of the detection, segmented images of the plant seedling have been automatically generated using a Mask Region-based Convolutional Neural Network (RCNN). Consequently, three models are utilised to detect BSR: a convolutional neural network that is 16 layers deep (VGG16) model trained on a segmented image; and VGG16 and Mask RCNN models both trained on the original images. The results indicate that the VGG16 model trained with the original images at 938 nm wavelength performed the best in terms of accuracy (91.93%), precision (94.32%), recall (89.26%), and F1 score (91.72%). This method revealed that users may detect BSR automatically without having to manually extract image attributes before detection.

Ganodermaboninense (G. boninense) is a fungus that causes one of the most destructive diseases in oil palm plantations in Southeast Asia called basal stem rot (BSR), resulting in annual losses of up to USD 500 million. The G. boninense infects both mature trees and seedlings. The current practice of detection still depends on manual inspection by a human expert every two weeks. This study aimed to detect early G. boninense infections using visible-near infrared (VIS-NIR) hyperspectral images where there are no BSR symptoms present. Twenty-eight samples of oil palm seedlings at five months old were used whereby 15 of them were inoculated with the G. boninense pathogen. Five months later, spectral reflectance oil palm leaflets taken from fronds 1 (F1) and 2 (F2) were obtained from the VIS-NIR hyperspectral images. The significant bands were identified based on the high separation between uninoculated (U) and inoculated (I) seedlings. The results indicate that the differences were evidently seen in the NIR spectrum. The bands were later used as input parameters for the development of Support Vector Machine (SVM) classification models, and these bands were optimized according to the classification accuracy achieved by the classifiers. It was observed that the U and I seedlings were excellently classified with 100% accuracy using 35 bands and 18 bands of F1. However, the combination of F1 and F2 (F12) gave better accuracy than F2 and almost similar to F1 for specific classifiers. This finding will provide an advantage when using aerial images where there is no need to separate F1 and F2 during the data pre-processing stage.

The Tropical Rainfall Measuring Mission (TRMM) was the first Earth Science mission dedicated to studying tropical and subtropical rainfall. Up until now, there is still limited knowledge on the accuracy of the version 7 research product TRMM 3B42-V7 despite having the advantage of a high temporal resolution and large spatial coverage over oceans and land. This is particularly the case in tropical regions in Asia. The objective of this study is therefore to analyze the performance of rainfall estimation from TRMM 3B42-V7 (henceforth TRMM) using rain gauge data in Malaysia, specifically from the Pahang river basin as a case study, and using a set of performance indicators/scores. The results suggest that the altitude of the region affects the performances of the scores. Root Mean Squared Error (RMSE) is lower mostly at a higher altitude and mid-altitude. The correlation coefficient (CC) generally shows a positive but weak relationship between the rain gauge measurements and TRMM (0 < CC < 0.4), while the Nash-Sutcliffe Efficiency (NSE) scores are low (NSE < 0.1). The Percent Bias (PBIAS) shows that TRMM tends to overestimate the rainfall measurement by 26.95% on average. The Probability of Detection (POD) and Threat Score (TS) demonstrate that more than half of the pixel-point pairs have values smaller than 0.7. However, the Probability of False Detection (POFD) and False Alarm Rate (FAR) show that most of the pixel-point gauges have values lower than 0.55. The seasonal analysis shows that TRMM overestimates during the wet season and underestimates during the dry season. The bias adjustment shows that Mean Bias Correction (MBC) improved the scores better than Double-Kernel Residual Smoothing (DS) and Residual Inverse Distance Weighting (RIDW). The large errors imply that TRMM may not be suitable for applications in environmental, water resources, and ecological studies without prior correction.