Zobrazeno 1 - 10
of 121
pro vyhledávání: '"Pazhanivelan P"'
Autor:
Koushika, Suganthi Pazhanivel, Krishnaveni, Anbalagan, Pazhanivelan, Sellaperumal, Bharani, Alagirisamy, Arunkumar, Venugopal, Devaki, Perumal, Muthukrishnan, Narayanan
The loss of soil organic carbon (SOC) poses a severe danger to agricultural sustainability around the World. This review examines various farming practices and their impact on soil organic carbon storage. After a careful review of the literature, mos
Externí odkaz:
http://arxiv.org/abs/2403.07530
Autor:
S. Marimuthu, S. Vallal Kannan, S. Pazhanivelan, V. Geethalakshmi, M. Raju, A. P. Sivamurugan, M. Karthikeyan, V. M. Byrareddy, S. Mushtaq, U. Surendran
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract Blackgram, a protein-rich pulse crop (24%), is crucial for combating food insecurity, particularly in malnourished and economically weak countries. Enhancing blackgram production requires improved, input-saving management practices. Given th
Externí odkaz:
https://doaj.org/article/792c4e4cfed44f73b41b11bed89fbde9
Publikováno v:
Journal of Agricultural Engineering, Vol 55, Iss 4 (2024)
YOLO represents the one-stage object detection also called regression-based object detection. Object in the given input is directly classified and located instead of using the candidate region. The accuracy from two-stage detection is higher than one
Externí odkaz:
https://doaj.org/article/d4c6b2153f4746bf92af481b98174821
Autor:
Ratchagar Arockia Infant Paul, Murali Arthanari Palanisamy, Panneerselvam Peramaiyan, Virender Kumar, Muthukumar Bagavathiannan, Bholuram Gurjar, Shanmugam Vijayakumar, Maduraimuthu Djanaguiraman, Sellaperumal Pazhanivelan, Kavitha Ramasamy
Publikováno v:
Frontiers in Agronomy, Vol 6 (2024)
Unmanned aerial vehicles (UAVs) represent a cutting-edge technology that holds the promise of revolutionizing the conventional tasks carried out in the realm of agriculture. On a global scale, UAVs are gaining prominence for pesticide applications, p
Externí odkaz:
https://doaj.org/article/5cb3e4bb99ee4784ad204c0e4290b13b
Autor:
NARAYANASWAMY JEEVAN, SELLAPERUMAL PAZHANIVELAN, RAMALINGAM KUMARAPERUMAL, A P SIVAMURUGAN, MRUNALINI KANCHETI
Publikováno v:
The Indian Journal of Agricultural Sciences, Vol 94, Iss 11 (2024)
The experiment was conducted during summer and rainy (kharif) seasons of 2021 and 2022 at Agricultural Research Station (Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu), Bhavanisagar, Tamil Nadu to evaluate the efficiency, economics and e
Externí odkaz:
https://doaj.org/article/e5a7b07d4604415aa1c43ee80ac1989b
Publikováno v:
The Indian Journal of Agricultural Sciences, Vol 94, Iss 11 (2024)
The experiment was conducted during rainy (kharif) seasons of 2020 and 2021 at Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu to study the efficacy of new generation herbicide mixtures in irrigated maize (Zea mays L.). The experiment was
Externí odkaz:
https://doaj.org/article/1bb8735e8b67432cbb2e7c130351585e
Publikováno v:
Agricultural Science and Technology, Vol 16, Iss 2, Pp 3-16 (2024)
Abstract. Plantation mapping plays a vital role in agriculture, forestry, and land management. The integration of Artificial intelligence and Machine learning techniques with high-resolution satellite data has revolutionized the accuracy and efficien
Externí odkaz:
https://doaj.org/article/152b64da5506438a9a336d8da92a3668
Autor:
Chinnu Raju, Sellaperumal Pazhanivelan, Irene Vethamoni Perianadar, Ragunath Kaliaperumal, N. K. Sathyamoorthy, Vaithiyanathan Sendhilvel
Publikováno v:
Agriculture, Vol 14, Iss 11, p 2018 (2024)
Climate change is an emerging threat to global food and nutritional security. The tropical fruits such as mango, bananas, passionfruit, custard apples, and papaya are highly sensitive to weather changes especially; changes of monsoon onset and elevat
Externí odkaz:
https://doaj.org/article/de68e607e7c34f6aaa48ec0e130200f1
Autor:
Thamizh Vendan Tarun Kshatriya, Ramalingam Kumaraperumal, Sellaperumal Pazhanivelan, Nivas Raj Moorthi, Dhanaraju Muthumanickam, Kaliaperumal Ragunath, Jagadeeswaran Ramasamy
Publikováno v:
Agronomy, Vol 14, Iss 11, p 2707 (2024)
Large-scale mapping of soil resources can be crucial and indispensable for several of the managerial applications and policy implications. With machine learning models being the most utilized modeling technique for digital soil mapping (DSM), the imp
Externí odkaz:
https://doaj.org/article/55f54f0584bc4a96874d952051fada1f
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