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.

Maize is an important industrial crop where yield and quality enhancement both assume greater importance. Clean production technologies like conservation agriculture and integrated nutrient management hold the key to enhance productivity and quality besides improving soil health and environment. Hence, maize productivity and quality were assessed under a maize–wheat cropping system (MWCS) using four crop-establishment and tillage management practices [FBCT–FBCT (Flat bed–conventional tillage both in maize and wheat); RBCT–RBZT (Raised bed–CT in maize and raised bed–zero tillage in wheat); FBZT–FBZT (FBZT both in maize and wheat); PRBZT–PRBZT (Permanent raised bed–ZT both in maize and wheat], and five P-fertilization practices [P100 (100% soil applied-P); P50 + 2FSP (50% soil applied-P + 2 foliar-sprays of P through 2% DAP both in maize and wheat); P50 + PSB + AM-fungi; P50 + PSB + AMF + 2FSP; and P0 (100% NK with no-P)] in split-plot design replicated-thrice. Double zero-tilled PRBZT–PRBZT system significantly enhanced the maize grain, starch, protein and oil yield by 13.1–19% over conventional FBCT–FBCT. P50 + PSB + AMF + 2FSP, integrating soil applied-P, microbial-inoculants and foliar-P, had significantly higher grain, starch, protein and oil yield by 12.5–17.2% over P100 besides saving 34.7% fertilizer-P both in maize and on cropping-system basis. P50 + PSB + AMF + 2FSP again had significantly higher starch, lysine and tryptophan content by 4.6–10.4% over P100 due to sustained and synchronized P-bioavailability. Higher amylose content (24.1%) was observed in grains under P50 + PSB + AMF + 2FSP, a beneficial trait due to its lower glycemic-index highly required for diabetic patients, where current COVID-19 pandemic further necessitated the use of such dietary ingredients. Double zero-tilled PRBZT–PRBZT reported greater MUFA (oleic acid, 37.1%), MUFA: PUFA ratio and P/S index with 6.9% higher P/S index in corn-oil (an oil quality parameter highly required for heart-health) over RBCT-RBCT. MUFA, MUFA: PUFA ratio and P/S index were also higher under P50 + PSB + AMF + 2FSP; avowing the obvious role of foliar-P and microbial-inoculants in influencing maize fatty acid composition. Overall, double zero-tilled PRBZT–PRBZT with crop residue retention at 6 t/ha per year along with P50 + PSB + AMF + 2FSP while saving 34.7% fertilizer-P in MWCS, may prove beneficial in enhancing maize productivity and quality so as to reinforce the food and nutritional security besides boosting food, corn-oil and starch industry in south-Asia and collateral arid agro-ecologies across the globe.

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.

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