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

Sandeep Kumar,Arpit Jain,Mehedi Masud,Rohit Raja,Satyendr Singh,Mohammed A Alzain,Shilpa Rani,G Harshitha,Anand Prakash Shukla,Dr Dilbag Singh

doi:10.1155/2021/1790171

Abstract

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.

Highlights

Day by day, all over the world, agriculture land is going to be reduced because the population is increasing rapidly and lack of water resources
After surveying the available research paper on a cotton disease, we have identified some of the gaps in the currently available work. ere are some gaps and findings which are discussed below: (i) Some of the techniques are time-consuming by late identification of the disease, and the process for the detection may be lengthy in few cases [57,58,59]
(iv) e available traditional and machine learning algorithms are giving accurate results, but it would be better if these algorithms could detect the diseases in the early stage [56, 61]

Summary

Introduction

All over the world, agriculture land is going to be reduced because the population is increasing rapidly and lack of water resources. Cotton is a fiber crop that fills in as a wellspring of feed, staple, biofuel production, and oilseed harvest, which provides 35% globally of the total fiber and raw material for producing textiles [6,7,8]. It is the most important and principal cash crop that affects India’s economy in many. Identification of the symptoms of TSV infection by visual observation of the plants often results in misdiagnosis as this virus matches those reflecting nutritional disorders affecting cotton [25].

Methods

Results

Discussion

Conclusion