Identification of a suitable machine learning model for detection of asymptomatic Ganoderma boninense infection in oil palm seedlings using hyperspectral data

In Malaysia, oil palm industry has made an enormous contribution to economic and social prosperity. However, it has been affected by basal stem rot (BSR) disease caused by Ganoderma boninense (G. boninense) fungus. The conventional practice to detect the disease is through manual inspection by a human expert every two weeks. This study aimed to identify the most suitable machine learning model to classify the inoculated (I) and uninoculated (U) oil palm seedlings with G. boninense before the symptoms’ appearance using hyperspectral imaging. A total of 1122 sample points were collected from frond 1 and frond 2 of 28 oil palm seedlings at the age of 10 months old, with 540 and 582 reflectance spectra extracted from U and I seedlings, respectively. The significant bands were identified based on the high separation between U and I seedlings, where the differences were observed significantly in the NIR spectrum. The reflectance values of each selected band were later used as input parameters of the 23 machine learning models developed using decision trees, discriminant analysis, logistic regression, naïve Bayes, support vector machine (SVM), k-nearest neighbor (kNN), and ensemble modelling with various types of kernels. The bands were optimized according to the classification accuracy achieved by the models. Based on the F-score and performance time, it was demonstrated that coarse Gaussian SVM with 9 bands performed better than the models with 35, 18, 14, and 11 bands. The coarse Gaussian SVM achieved an F-score of 95.21% with a performance time of 1.7124 s when run on a personal computer with an Intel® Core™ i7-8750H processor and 32 GB RAM. This early detection could lead to better management in the oil palm industry

Last decade assessment of the impacts of regional climate change on crop yield variations in the mediterranean region

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

Satellite rainfall (TRMM 3B42-V7) performance assessment and adjustment over Pahang River Basin, Malaysia

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