Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Savita Kolhe"'
Autor:
Vennampally Nataraj, Sanjay Gupta, K. H. Singh, Prince Choyal, Raghavendra Nargund, M. Shivakumar, Nisha Agrawal, Giriraj Kumawat, Vangala Rajesh, Rakesh Kumar Verma, Gyanesh K. Satpute, Bairi Srikanth, Savita Kolhe
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024)
Abstract Soybean is a rainfed crop grown across a wide range of environments in India. Its grain yield is a complex trait governed by many minor genes and influenced by environmental effects and genotype × environment interactions. In the current in
Externí odkaz:
https://doaj.org/article/dcae410e2b8948c4afa7215c85364d58
Publikováno v:
International Journal of Engineering Trends and Technology. 71:408-420
Publikováno v:
IOP Conference Series: Materials Science and Engineering. 1099:012037
Leaves express the initial symptoms of many diseases in soybean crop. CNN based Computer-Vision (CV) of the leaf-images identifies, enables the diagnosing and controlling the soybean-crop diseases. All three actions need proactive maintenance and Ope
Publikováno v:
Computers and Electronics in Agriculture. 178:105747
Ensemble methods give better performance compared to a single machine learning algorithm. Vote is one of the best ensembles. Vote merges predictions from Simple Logistics and the Naive Bayes algorithms in the present work. The paper presents a new en
Publikováno v:
Computers and Electronics in Agriculture. 124:65-72
New hybrid ensemble algorithm for multiclass classification problems is proposed.It uses machine learning algorithms, feature ranking method and an instance filter.Its aim is to improve the performance results of ensemble-Vote.It is tested on four st
Publikováno v:
IJARCCE. 5:140-143
Publikováno v:
IJIREEICE. 4:42-46
Publikováno v:
Expert Systems with Applications. 38:14592-14601
The paper reports an interface designed for three subsystems-the knowledge acquisition subsystem with dynamic disease knowledge base, intelligent disease diagnosis subsystem with object-oriented intelligent-inference model and intelligent tutor for c
Publikováno v:
Computers and Electronics in Agriculture. 76:16-27
This paper suggests a new approach for providing intelligence in the system for diagnosis of diseases of the oilseed-crops. It reports the development of a web-based intelligent disease diagnosis system (WIDDS). The WIDDS is based on a new fuzzy logi
Publikováno v:
NaBIC
This paper presents a knowledge management system for crop disease. The aim of KMSCD is to provide a knowledge management tool for efficient knowledge acquisition, storage, knowledge engineering, processing and proper maintenance of knowledge that ca