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.

Cotton is a key global fiber crop. However, yield potential is limited by the presence of endemic and introduced pests and diseases. The introduction of host plant resistance (HPR), defined as the purposeful use of resistant crop cultivars to reduce the impact of pests and diseases, has been a key breeding target for the Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding program. The program has seen success in releasing cultivars resistant to Bacterial blight, Verticillium wilt, Fusarium wilt, and Cotton bunchy top. However, emerging biotic threats such as Black root rot and secondary pests, are becoming more frequent in Australian cotton production systems. The uptake of tools and breeding methods, such as genomic selection, high throughput phenomics, gene editing, and landscape genomics, paired with the continued utilization of sources of resistance from Gossypium germplasm, will be critical for the future of cotton breeding. This review celebrates the success of HPR breeding activities in the CSIRO cotton breeding program and maps a pathway for the future in developing resistant cultivars.

This 3-page publication discusses the benefits of using perennial grasses in peanut/cotton rotations for integrated management of nematodes, diseases, and weeds. Written by D. L. Wright, J. J. Marois, S. George, Z. Grabau, R. Barocco, and I. Small, and published by the UF/IFAS Agronomy Department, revised September 2022. SS-AGR-125/AG257: Use of Perennial Grasses in Peanut/Cotton Rotations for Integrated Management of Nematodes, Diseases, and Weeds (ufl.edu)

This 3-page publication discusses the benefits of using perennial grasses in peanut/cotton rotations for integrated management of nematodes, diseases, and weeds. Written by D. L. Wright, J. J. Marois, S. George, Z. Grabau, R. Barocco, and I. Small, and published by the UF/IFAS Agronomy Department, revised September 2022. SS-AGR-125/AG257: Use of Perennial Grasses in Peanut/Cotton Rotations for Integrated Management of Nematodes, Diseases, and Weeds (ufl.edu)

Early diagnosis of cotton verticillium wilt (VW) and accurate assessment of the disease degree are important prerequisites for preventing the large-scale development of cotton VW. Hyperspectral techniques have been widely used for monitoring the extent of plant diseases, but early detection of VW disease in cotton remains a challenge. In this study, the Boruta algorithm was used to select the key physiological characteristics (leaf temperature, chlorophyll a content, and equivalent water thickness) of cotton leaves at the early stage of VW disease, and then the Relief-F algorithm was used to select the spectral features indicating multiple &ldquo:symptoms&rdquo: of cotton VW disease at the early stage. In addition, a new cotton VW early monitoring indicator (CVWEI) was constructed by combining the weights of the new index and related bands using a hierarchical analysis (AHP) and entropy weighting method (EWM). The study showed that the physiological indices constructed under VW stress were better indicators of VW disease than traditional vegetation indices: CVEWI achieved a high accuracy of 95% in the test set, with a Kappa coefficient of 0.89: and the test set R2 was 0.73 and RMSE was 3.15% for monitoring disease severity, compared to the optimal classification constructed using a single spectral index. The results may provide new ideas and methods for early and accurate monitoring of VW and other fungal diseases.

Cotton (Gossypium spp. L., Malvaceae) is the world’s largest source of natural fibers. Virus outbreaks are fast and economically devasting regarding cotton. Identifying new viruses is challenging as virus symptoms usually mimic nutrient deficiency, insect damage, and auxin herbicide injury. Traditional viral identification methods are costly and time-consuming. Developing new resistant cotton lines to face viral threats has been slow until the recent use of molecular virology, genomics, new breeding techniques (NBT), remote sensing, and artificial intelligence (AI). This perspective article demonstrates rapid, sensitive, and cheap technologies to identify viral diseases and propose their use for virus resistance breeding.

Abstract Cotton is the prime natural fiber with economic significance globally. Cotton farming and breeding have a long history in Pakistan. The development of high yielding upland cotton (Gossypium hirsutum) varieties gradually replaced the cultivation of diploid Gossypium species. Climate change along with emergence of new epidemic diseases caused yield loss in recent years. The biotic stress considerably reduced the performance and yield potential of cotton. Suitable breeding strategies are essential to generate useful genetic variations and to identify desired traits. Conventional breeding has remarkably increased cotton yield and fiber quality, which has cultivated the NIAB-78, S-12, MNH‐786, and FH‐Lalazar like cultivars. However, this phenotypic selection based breeding method has low efficiency to produce stress resilient cotton. The efficiency of traditional breeding has significantly improved by the marker assisted selection technology. Breakthroughs in molecular genetics, bioinformatics analysis, genetic engineering, and genome sequencing have opened new technique routes for cotton breeding. In addition, genetic improvement through quantitative trait loci, transcriptome, and CRISPR/Cas9 mediated genomic editing can provide suitable platform to improve the resistance to stresses induced by bollworms, cotton leaf curl virus, heat, drought, and salt. The approval of transgenic lines harboring triple gene Cry1Ac + Cry2A + GTG are critical for cotton crop. This review has critically discussed the progress and limitations of cotton breeding in Pakistan, and reviewed the utilization of novel genetic variations and selection tools for sustainable cotton production.