Light signaling and UV-B-mediated plant growth regulation^FA

Light plays an important role in plants' growth and development throughout their life cycle. Plants alter their morphological features in response to light cues of varying intensity and quality. Dedicated photoreceptors help plants to perceive light signals of different wave-lengths. Activated photoreceptors stimulate the down-stream signaling cascades that lead to extensive gene expression changes responsible for physiological and developmental responses. Proteins such as ELONGATED HYPOCOTYL5 (HY5) and CONSTITUTIVELY PHOTO-MORPHOGENIC 1 (COP1) act as important factors which modulate light-regulated gene expression, especially during seedling development. These factors function as central regulatory intermediates not only in red, far-red, and blue light pathways but also in the UV-B signaling pathway. UV-B radiation makes up only a minor fraction of sunlight, yet it imparts many positive and negative effects on plant growth. Studies on UV-B perception, signaling, and response in plants has considerably surged in recent times. Plants have developed different strat-egies to use UV-B as a developmental cue as well as to withstand high doses of UV-B radiation. Plants' re-sponses to UV-B are an integration of its cross-talks with both environmental factors and phytohormones. This review outlines the current developments in light sig-naling with a major focus on UV-B-mediated plant growth regulation.

Enhanced Field-Based Detection of Potato Blight in Complex Backgrounds Using Deep Learning

Rapid and automated identification of blight disease in potato will help farmers to apply timely remedies to protect their produce.Manual detection of blight disease can be cumbersome and may require trained experts.To overcome these issues,we present an automated system using the Mask Region-based convolutional neural network(Mask R-CNN)architecture,with residual network as the backbone network for detecting blight disease patches on potato leaves in field conditions.The approach uses transfer learning,which can generate good results even with small datasets.The model was trained on a dataset of 1423 images of potato leaves obtained from fields in different geographical locations and at different times of the day.The images were manually annotated to create over 6200 labeled patches covering diseased and healthy portions of the leaf.The Mask R-CNN model was able to correctly differentiate between the diseased patch on the potato leaf and the similar-looking background soil patches,which can confound the outcome of binary classification.To improve the detection performance,the original RGB dataset was then converted to HSL,HSV,LAB,XYZ,and YCrCb color spaces.A separate model was created for each color space and tested on 417 field-based test images.This yielded 81.4%mean average precision on the LAB model and 56.9%mean average recall on the HSL model,slightly outperforming the original RGB color space model.Manual analysis of the detection performance indicates an overall precision of 98%on leaf images in a field environment containing complex backgrounds.