Review: Weak radiofrequency radiation exposure from mobile phone radiation on plants

Abstract

The available literature on the effect of RF-EMFs on plants to date observed the significant trend of radiofrequency radiation influence on plants. Hence, this study provides new evidence supporting our hypothesis. Nonetheless, this endorses the need for more experiments to observe the effects of RF-EMFs, especially for the longer exposure durations, using the whole organisms. The above observation agrees with our earlier study, in that it supported that it is not a well-grounded method to characterize biological effects without considering the exposure duration. Nevertheless, none of these findings can be directly associated with human; however, on the other hand, this cannot be excluded, as it can impact the human welfare and health, either directly or indirectly, due to their complexity and varied effects (calcium metabolism, stress proteins, etc.). This study should be useful as a reference for researchers conducting epidemiological studies and the long-term experiments, using whole organisms, to observe the effects of RF-EMFs.

Machine Learning for Bioelectromagnetics: Prediction Model using Data of Weak Radiofrequency Radiation Effect on Plants

“…As explained in our previous review study (Halgamuge, 2016), in this study, physiological or morphological effects of plants (bio-effects) or plant response (changed/unchanged or effect/no effect) due to exposure to weak radiofrequency radiation from mobile phones and base station is defined as the changes in 1) plant growth rate; 2) seed germination rate (primary shoot and root length); 3) thermographic imaging; 4) carbohydrate metabolism; 5) oxidative damage/stress; 6) gene expression; 7) DNA damage; 8) reactive oxygen species (ROS); 9) cell function, enzyme activities; 10) mitotic index and mitotic abnormalities; 11) mutation rates and genomic stability; 12) pigmentation (chlorophyll concentration); and 13) chromosomal aberrations and micronuclei. The list of symbols is defined in Table 1 and attributes that we used for this analysis are shown in Table 2.…”

“…2 and Pea species are more sensitive to RF-EMFs (p < 0.0001). These values were then compared with the results from our previous review study [1] and observed similar behaviors. In our previous review, we found Maize, Roselle, Pea, Fenugreek, Duckweeds, Tomato, Onions and Mungbean plants are more sensitive to RF-EMF (p < 0.0001).…”

“…This approach shows changed/unchanged levels by using big data analytics and machine learning concept in bioelectromagnetics domain to reveal hidden patterns and unknown correlations. We used rawdata of plant exposure from our previous work [1] (extracted data from 45 peer-reviewed scientific publications published between 1996-2016 with 169 experimental case studies carried out in the scientific literature) and performed predictions, obtaining high-level of knowledge from raw data.…”

“…This paper used raw-data of plant exposure from our previous review study [1] (extracted data set from 45 peer-reviewed scientific publications (1996-2016) with 169 experimental observations carried out in the scientific literature, e.g. [23] and performed prediction extracting high levels of knowledge from raw data using different classification algorithms and performance evaluation methods.…”

“…Modeling plant sensitivity due to RF-EMF is an important task for both agriculture sector and for epidemiologist, on the other hand, it is a useful tool to assist a better understanding of this phenomenon and eventually advance it. Reported studies showed significant effects on plants that exposed to the radiofrequency radiation or plant sensitivity to the RF-EMF [1].…”