A Comparative Analysis of Machine Learning Algorithms for Detection of Organic and Nonorganic Cotton Diseases

被引:28
|
作者
Kumar, Sandeep [1 ]
Jain, Arpit [2 ]
Shukla, Anand Prakash [3 ]
Singh, Satyendr [4 ]
Raja, Rohit [5 ]
Rani, Shilpa [6 ]
Harshitha, G. [1 ]
AlZain, Mohammed A. [7 ]
Masud, Mehedi [8 ]
机构
[1] Sreyas Inst Engn & Technol, Hyderabad, India
[2] Teerthanker Mahaveer Univ, Moradabad, UP, India
[3] KIET Grp Inst, Gaziabad, India
[4] BML Munjal Univ, Gurugram, India
[5] Cent Univ, Bilaspur, Chhattisgarh, India
[6] Neil Gogte Inst Technol, Hyderabad, India
[7] Taif Univ, Dept Informat Technol, Coll Comp & Informat Technol, POB 11099, At Taif 21944, Saudi Arabia
[8] Taif Univ, Dept Comp Sci, Coll Comp & Informat Technol, POB 11099, At Taif 21944, Saudi Arabia
关键词
LEAF; INFECTION; AIRBORNE; GROWTH;
D O I
10.1155/2021/1790171
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Cotton is the natural fiber produced, and the commercial crop grown in monoculture on 2.5% of total agricultural land. Cotton is a drought-resistant crop that provides a reliable income to the farmers that grow under the area with a threat from climatic change. These cotton crops are being affected by bacterial, fungal, viral, and other parasitic diseases that may vary due to the climatic conditions resulting in the crop's low productivity. The most prone to diseases is the leaf that results in the damage of the plant and sometimes the whole crop. Most of the diseases occur only on leaf parts of the cotton plant. The primary purpose of disease detection has always been to identify the diseases affecting the plant in the early stages using traditional techniques for better production. To detect these cotton leaf diseases appropriately, the prior knowledge and utilization of several image processing methods and machine learning techniques are helpful.
引用
收藏
页数:18
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