Management of Potato Nematodes:An overview

Root-knot nematodes and cyst nematodes are important constraints that reduce potato yields in India. Three species of Meloidogyne cause root-knots on the crop throughout the country, of which, M. incognita is more wide-spread. Infected tubers also result in marketable-yield-loss particularly in the seed potatoes. The cyst nematodes include two species of Globodera restricted to the hilly regions of Tamil Nadu and are of quarantine importance, inhibiting seedpotato production. Potato produce from these hills is used only for consumption. The endoparasitic nature of their life cycle, deposition of eggs into a gelatinous egg mass in root knot and the female turning in to a hard cyst encompassing the eggs within them in cyst nematode makes them difficult organisms to manage. Both these nematodes exhibit physiologic variation, hence, their management is not absolute with host-resistance. Therefore, an Integrated Nematode Management (INM) is adopted in both the cases. Root-knot nematode in North India is managed using nematode-free seed tubers, crop rotation with maize or wheat and application of 1-2 kg ai /ha Carbofuran 3% G at the time of potato planting. Cyst nematode in Tamil Nadu hills is managed by crop rotation with vegetables, particularly cabbage and carrot, intercropping potato with beans or wheat, alternating nematode resistant potato variety 'Kufri Swarna' and application of 2 kg ai /ha Carbofuran 3% G at planting. A two-year adoption of INM for root-knot and a three-year INM practice for cyst nematodes gives efficient and economical production system. Potato farmers in Himachal Pradesh and Tamil Nadu hills follow practices standardized at the Central Potato Research Institute, Shimla and it's substation in the Nilgiri hills

CNN Ensemble Approach for Early detection of Sugarcane Diseases – A Comparison

This paper mainly concentrates and discusses on sugarcane crop, the variety of cane seeds available for sowing; various cane diseases and its early detection using different approaches. Machine Learning (ML) and Deep Learning (DL) techniques are used to analyze agricultural data like temperature, soil quality, yield prediction, selling price forecasts, etc. and avoid crop damage from a variety of sources, including diseases. In the proposed work, with particular reference to eight specific sugarcane crop diseases and including healthy crop database, the neural network algorithms are tested and verified in terms quality metrics like accuracy, F1 score, recall and precision

Facilitation of peptide fibre formation by arginine-phosphate/carboxylate interactions

This study describes peptide fibre formation in a hexapeptide, derived from the V3 loop of HIV-1, mediated by the interactions between arginine residues and phosphate/carboxylate anions. This charge neutralization approach was further confirmed when the deletion of arginine residue from the hexapeptide sequence resulted in fibre formation, which was studied by a combination of microscopic techniques