Characterizing the Potato Growing Regions in India Using Meteorological Parameters.

Currently, the multi-location testing of advanced hybrids in India is carried out at 25 centers under the All India Co-ordinated Research Project on Potato (AICRP-P), which is spread across the country. These centres have been chosen to represent different potato growing regions based on soil and agronomic features. However, the reliable deployment of the newly bred varieties in different regions requires a scientific delineation of potato growing zones with homogenous climates. The present study was undertaken to develop homogenous zones in the Indian sub-continent based on the environmental parameters of the potato growing season. A total of 1253 locations were identified across the country as having a plausible potato growing season of at least 70 days with suitable thermal limits. Six variables including five meteorological parameters including Physiological days (P days), Growing degree days (GDD), Mean daily temperature, Mean night temperature and Mean daily incident solar radiation, together with altitude as the sixth variable, were used for Agglomerative Hierarchical Clustering (AHC) and the Principal Component Analysis by Multidimensional Scaling (MDS) technique to derive identical classes. The thematic map of the classes was overlaid on potato growing districts of India using ArcGIS 9.1 software. The study clearly depicted that the clustering technique can effectively delineate the target population of environments (TPE) for potato genotypes performing well at different testing environments in India. The study also identifies target locations for future focus on breeding strategies, especially the high night temperature class having a large expanse in India. This is also vital in view of the impending climate change situation.

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.

Transcriptome analysis of potato shoots, roots and stolons under nitrogen stress.

Potato crop requires high dose of nitrogen (N) to produce high tuber yield. Excessive application of N causes environmental pollution and increases cost of production. Hence, knowledge about genes and regulatory elements is essential to strengthen research on N metabolism in this crop. In this study, we analysed transcriptomes (RNA-seq) in potato tissues (shoot, root and stolon) collected from plants grown in aeroponic culture under controlled conditions with varied N supplies i.e. low N (0.2 milli molar N) and high N (4 milli molar N). High quality data ranging between 3.25 to 4.93 Gb per sample were generated using Illumina NextSeq500 that resulted in 83.60-86.50% mapping of the reads to the reference potato genome. Differentially expressed genes (DEGs) were observed in the tissues based on statistically significance (p ≤ 0.05) and up-regulation with ≥ 2 log2 fold change (FC) and down-regulation with ≤ -2 log2 FC values. In shoots, of total 19730 DEGs, 761 up-regulated and 280 down-regulated significant DEGs were identified. Of total 20736 DEGs in roots, 572 (up-regulated) and 292 (down-regulated) were significant DEGs. In stolons, of total 21494 DEG, 688 and 230 DEGs were significantly up-regulated and down-regulated, respectively. Venn diagram analysis showed tissue specific and common genes. The DEGs were functionally assigned with the GO terms, in which molecular function domain was predominant in all the tissues. Further, DEGs were classified into 24 KEGG pathways, in which 5385, 5572 and 5594 DEGs were annotated in shoots, roots and stolons, respectively. The RT-qPCR analysis validated gene expression of RNA-seq data for selected genes. We identified a few potential DEGs responsive to N deficiency in potato such as glutaredoxin, Myb-like DNA-binding protein, WRKY transcription factor 16 and FLOWERING LOCUS T in shoots; high-affinity nitrate transporter, protein phosphatase-2c, glutaredoxin family protein, malate synthase, CLE7, 2-oxoglutarate-dependent dioxygenase and transcription factor in roots; and glucose-6-phosphate/phosphate translocator 2, BTB/POZ domain-containing protein, F-box family protein and aquaporin TIP1;3 in stolons, and many genes of unknown function. Our study highlights that these potential genes play very crucial roles in N stress tolerance, which could be useful in augmenting research on N metabolism in potato.