The interplay between polyamines (PAs) and nitrogen (N) is emerging as a key factor in plant response to abiotic and biotic stresses. The PA/N interplay in plants connects N metabolism, carbon (C) fixation, and secondary metabolism pathways. Glutamate, a pivotal N-containing molecule, is responsible for the biosynthesis of proline (Pro), arginine (Arg) and ornithine (Orn) and constitutes a main common pathway for PAs and C/N assimilation/incorporation implicated in various stresses. PAs and their derivatives are important signaling molecules, as they act largely by protecting and preserving the function/structure of cells in response to stresses. Use of different research approaches, such as generation of transgenic plants with modified intracellular N and PA homeostasis, has helped to elucidate a plethora of PA roles, underpinning their function as a major player in plant stress responses. In this context, a range of transgenic plants over-or under-expressing N/PA metabolic genes has been developed in an effort to decipher their implication in stress signaling. The current review describes how N and PAs regulate plant growth and facilitate crop acclimatization to adverse environments in an attempt to further elucidate the N-PAs interplay against abiotic and biotic stresses, as well as the mechanisms controlling N-PA genes/enzymes and metabolites.

In this research a non-destructive, rapid and cost effective examination machine for the estimation of the ripeness fraction, oil content and free fatty acid level in oil palm fresh fruits bunch was developed. The automatic machine-vision based inspection system provided consistency, rapid estimation and acceptable accuracy results in non-destructive manner. Fresh fruits bunch samples from Tenera cultivar (7 to 20 years trees) were taken from Cimulang plantation, Bogor, Indonesia. Two statistical analysis methods were used: a forward stepwise multiple linear regression analysis and a multilayer-perceptron artificial neural network analysis. The best prediction of ripeness and oil content models were obtained using the latter method, while the best free fatty acid prediction model was developed by the first method. The models were then employed in the machine-vision inspection systems of the machine. The system best prediction accuracy of ripeness, oil content and free fatty acid models was 93.5, 96.41, and 89.32%, with standard error of prediction being 0.065, 0.044 and 0.068, respectively. The system was tested through a series of field tests, and successfully examined more than 12 t of fruits bunch per hour, without causing damage.

Color considered as a main character to determine quality of agricultural products, especially fruit ripeness. Human eyes are excellent in differentiating color, however, human perception to color are often inconsistent, influence by their physical and psychological state. In this study, the ripeness of oil palm fresh fruits bunch (FFB) assessed using a portable and low-cost device, comprised of digital camera, laptop and a small and lightweight chamber equipped with independent LED lights. First, the FFB sample was observed by three experts to evaluate its ripeness. Then the sample placed inside the chamber and recorded by camera. In order to record the whole bunch, camera was positioned perpendicularly 1 meter above the ground, facing down toward the FFB. The recorded FFB image subsequently segmented and analyzed using the image processing software in the computer. The software calculated and specified the color of the FFB image in RGB color space. The results then compared with the observations by the panelists. In this study, FFB color observations by the camera vision, produced better consistency compare to the observation results of from the experts

Optical Characteristics of Oil Palm Fresh Fruits Bunch (FFB) Under Three Spectrum Regions Influence for Harvest Decision