Towards an intelligent approaches for cotton diseases detection: A review

Abstract

Cotton is one of the leading fibers and plays a dominant role in the global industrial and agricultural economy. It is a primary material for the textile industry production. Various cotton leaf diseases include Bacteria blight, Foliar disease, Alternaria, etc. decrease the mass cotton production gain and quality. Hence, early diagnosis is demanded to avoid the ailments on cotton plants' leaves to increase productivity. The monitoring of cotton leaf disease and plants' health is complicated in farmers' naked eyes based on their own acquired knowledge and experience. It is expensive and impossible all-time for large plantation areas and leads to inaccurate control measurements of pesticides. The monitoring of the bugs and attacks in cotton plants is a sarcastic task for agriculture sustainability. Information on several diseases and syndrome can assist the farmers in determining the right pest control strategies to regulate diseases to improve cotton productivity. The study results betray that the available automated identification methods for cotton crop diseases are still in infancy. This review recognizes that automatic, economical, reliable, accurate, and rapid diagnosis systems are needed for cotton leaf disease discovery to increase production and quality. In this view, this paper exhibits an in-depth methodological review of various computational methods operated in different stages of plant-pathogen systems like image preprocessing, segmentation, feature extraction and selection, and classification to diagnosis the diseases for increasing cotton production. The issues behind the computational approaches of plant pathogens are addressed in-depth. The strengths and weaknesses of the state-of-art method in literature are highlighted. Further, the research issues also presented with valid future directions and further scope. Hence, novel, fully automatic computer-assisted systems are demanded to detect and classify numerous diseases in cotton plants.