Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images BS Anami, NN Malvade, S Palaiah Artificial intelligence in agriculture 4, 12-20, 2020 | 120 | 2020 |
Classification of yield affecting biotic and abiotic paddy crop stresses using field images BS Anami, NN Malvade, S Palaiah Information Processing in Agriculture 7 (2), 272-285, 2020 | 85 | 2020 |
Zinc biofortified rice varieties: challenges, possibilities, and progress in India D Sanjeeva Rao, CN Neeraja, P Madhu Babu, B Nirmala, K Suman, ... Frontiers in Nutrition 7, 26, 2020 | 78 | 2020 |
Automated recognition and classification of adulteration levels from bulk paddy grain samples BS Anami, NN Malvade, S Palaiah Information processing in agriculture 6 (1), 47-60, 2019 | 45 | 2019 |
Classification of rice diseases using convolutional neural network models R Yakkundimath, G Saunshi, B Anami, S Palaiah Journal of The Institution of Engineers (India): Series B 103 (4), 1047-1059, 2022 | 44 | 2022 |
Variability studies for seed and seedling traits in Pongamia pinnata (L.) Pierre. VMP Patil, H Shivanna, P Surendra, GO Manjunath, A Krishna, GV Dasar | 30 | 2011 |
Effect of plant growth regulators and micronutrients on yield and yield components in okra. P Surendra, CM Nawalagatti, MB Chetti, SM Hiremath | 30 | 2006 |
Genetic variability for yield and yield attributing traits in F3 generation of rice (Oryza sativa L.) M Mallimar, P Surendra, NG Hanamaratti, M Jogi, TN Sathisha, ... Research in Environment and Life Sciences 9 (1), 24-28, 2015 | 21 | 2015 |
Combining ability studies in pearl millet D Lakshmana, P Surendra, R Gurumurthy Res. on Crops 4 (3), 358-362, 2003 | 17 | 2003 |
Automatic methods for classification of visual based viral and bacterial disease symptoms in plants R Yakkundimath, G Saunshi, S Palaiah International Journal of Information Technology 14 (1), 287-299, 2022 | 16 | 2022 |
Seed quality enhancement techniques NT Komala, GM Sumalatha, R Gurumurthy, P Surendra Journal of Pharmacognosy and Phytochemistry 7 (1S), 3124-3128, 2018 | 10 | 2018 |
Morphological characterization of advance lines of rice (Oryza sativa L.) derived from swarna x ranbir basmati at seedling stage NT Komala, R Gurumurthy, P Surendra Journal of Rice Research 10 (1), 27-36, 2017 | 9 | 2017 |
Genetic variability studies in pearl millet (Pennisetum glaucum L.) genotypes. D Lakshmana, P Surendra, R Gurumurty | 8 | 2003 |
Variability, Correlation and Path Coefficient Analysis of Rice (Oryza sativa L.). Tribhuvan University Institute of Agriculture and Animal Science, Gokuleshwor, Baitadi, Nepal P Binod, A Niranjan, S Saugat, P Surendra International Journal of Scientific & Engineering Research 7, 2107-2176, 2016 | 7 | 2016 |
Classification of yield affecting biotic and abiotic paddy crop stresses using field images. Information Processing in Agriculture 7, 272–285 BS Anami, NN Malvade, S Palaiah | 6 | 2020 |
Correlation studies for micronutrients, yield and yield components in F3 population of rice (Oryza Sativa L.) M Mallimar, P Surendra, R Hundekar, M Jogi, MCS Lakkangoudar Res Environ Life Sci 9 (9), 1140-1142, 2016 | 6 | 2016 |
Effect of plant growth regulators and micronutrients on morpho-physiological and biochemical traits and yield in okra. P Surendra, CM Nawalagatti, MB Chetti, SM Hiremath | 6 | 2006 |
Combining ability studies in pearl millet (Pennisetum glaucum L.). D Lakshmana, P Surendra, R Gurumurty | 6 | 2003 |
Zinc biofortified rice varieties: challenges, possibilities, and progress in India. Front Nutr 7: 26 SD Rao, CN Neeraja, P Madhu Babu, B Nirmala, K Suman, LVS Rao, ... | 5 | 2020 |
OMICS technologies towards seed quality improvement NT Komala, R Gurumurthy, P Surendra Int J Pure App Biosci 5, 1075-85, 2017 | 5 | 2017 |