Postharvest losses of fruits and vegetables present a major challenge in developing countries due to limited infrastructure and widespread use of synthetic chemicals, leading to food waste, economic decline, and health hazards. Plant extracts rich in bioactive compounds such as phenolics, flavonoids, alkaloids, and essential oils have emerged as promising, eco-friendly options for preserving and improving the quality of horticultural crops after harvest. This overview consolidates current knowledge on the chemical diversity, functional applications, and cellular mechanisms of plant extracts in preventing spoilage, slowing ripening, and maintaining the nutritional and sensory qualities of fruits and vegetables. Comparative analyses in selected developing nations highlight the seriousness of postharvest losses and the necessity for environmentally sustainable solutions. The review examines recent advancements, practical applications, and challenges like performance inconsistency, standardization, and scalability related to the use of botanical extracts. Future outlooks focus on integrating plant extracts with emerging technologies and ensuring these solutions reach smallholder farmers. Overall, plant extracts offer a sustainable route to environmentally friendly and safer postharvest management, which can bolster food security and foster responsible agriculture in the developing world. To unlock their full potential, further research should aim to develop standardized extraction techniques, optimize formulations, and enhance policy and technological support for large-scale implementation in developing countries.
In this paper, we propose a new algorithm to detect rear-approaching vehicle using frame structure similarity based on deep learning algorithm for use in agricultural machinery systems. The commonly used deep learning models well detect various types of vehicles and detect the shapes of vehicles from various camera angles. However, since the vehicle detection system for agricultural machinery needs to detect only a vehicle approaching from the rear, when a general deep learning model is used, a false positive is generated by a vehicle running on the opposite side (passing vehicle). In this paper, first, we use Faster R-CNN model that shows excellent accuracy rate in deep learning for vehicle detection. Second, we proposed an algorithm that uses the structural similarity and the root mean square comparison method for the region of interest(vehicles area) which is detected by Faster R-CNN between the coming vehicle and the passing vehicle. Experimental results show that the proposed method has a detection rate of 98.2% and reduced the false positive values, which is superior to general deep learning method.
Introduction: This study aimed to characterize the chemical and biological properties of Ocimum lamiifolium Hochst. ex Benth. (family: Lamiaceae) leaf extracts and explore their potential applications. Methods: Plant leaves were extracted with ethanol via maceration and hydrodistillation, yielding 5.95% (w/w) and 2.25% (v/w) extracts, respectively. The chemical compositions were identified using gas chromatography-mass spectrometry (GC-MS) and Fourier-transform infrared spectroscopy (FTIR). Using 1,1-diphenyl-2-picrylhydrazyl (DPPH) scavenging assays, both the ethanolic extract and the essential oil were evaluated for antioxidant activity. Results: Thirty compounds, primarily monoterpenoids, accounted for 81.13% of the total components of the O. lamiifolium essential oil. 55 compounds were identified in the crude ethanolic extract, accounting for 99% of the total. Hydrocarbon terpenes were particularly abundant in the crude ethanolic extract. The plant extracts showed promising antioxidant activity, with the EO and EE extracts having IC50 values of 1.56 and 2.52 μg/mL, respectively, compared with the reference stand-ard ascorbic acid, which had an IC50 of 3.90 μg/mL. Discussion: Compared to the EO extract, the EE extract exhibited larger inhibition zones. The antimicrobial results suggest that the EE extract contains more active components, as proven by the GC-MS results. Molecular docking analysis of the three identified components revealed that δ-cadinene exhibited the highest binding affinity and formed hydrophobic interactions with 8 amino acids. This implies that these compounds may function as DNA gyrase inhibitors. Conclusion: O. lamiifolium extracts have potential applications as antibacterial and antioxidant agents.
