The agricultural industry places a high priority on crop protection, especially when it comes to protecting cotton crops against pink bollworm infestations. Rapid disease identification is essential for effective crop management at the same time. In order to overcome these difficulties, this study offers a thorough process for utilizing the Robot Operating System (ROS) to deploy an autonomous robot. The robot is intended to identify infestations of pink bollworm in addition to counting and tracking red Disease that impact cotton harvests. Drones are used to check the condition of cotton fields, and the autonomous robot's path planning component is closely related to this process. Large fields are photographed by these drones in multispectral mode, and the robot's course planning technique is done by calculating the Normalized Difference Vegetation Index (NDVI) based on these images. This approach ensures targeted surveillance and intervention, optimizing the use of resources. A customized dataset was created especially for this application to improve the robot's detecting abilities. The dataset was utilized to train a YOLOV8 model, a state-of-the-art object detection architecture. Performance characteristics of the trained model are impressive; it has a mean Average Precision (mAP) of 67.1%, Precision of 67.9%, and Recall of 61.8%. These metrics highlight how well the model works to precisely locate and measure interesting occurrences in the cotton fields. In order to address the particular challenges presented by pink bollworm infestations and crop diseases in the context of cotton cultivation, this research contributes a comprehensive solution for autonomous crop monitoring and protection by seamlessly integrating ROS, drones, NDVI calculations, and a robust detection model.

Bollworm poses a significant threat to cotton crops worldwide, including in the Fergana province of Uzbekistan. This study aims to explore innovative approaches to bollworm control and contribute to the global understanding of integrated pest management strategies (IPM). Using an extensive literature review and field observations, the study evaluates the effectiveness of an integrated protection system in reducing reliance on chemical inputs while increasing overall efficiency. The results highlight the multigenerational life cycle of bollworm, its impact on various crops, and the need for artificial biological control methods and targeted insecticide applications. In addition, the study highlights the need to adopt globally accepted IPM practices to ensure sustainable pest management. The findings underscore the importance of collaboration among researchers, farmers, policymakers, and industry representatives in developing and implementing advanced control measures. Through integrated approaches and the incorporation of scientific advances, bollworm infestations can be contained while safeguarding agricultural productivity and minimizing environmental risks.

Members of the insect ATP binding cassette transporter subfamily C2 (ABCC2) in several moth species are known as receptors for the Cry1Ac insecticidal protein from Bacillus thuringiensis (Bt). Mutations that abolish the functional domains of ABCC2 are known to cause resistance to Cry1Ac, although the reported levels of resistance vary widely depending on insect species. In this study, the function of the ABCC2 gene as putative Cry1Ac receptor in Helicoverpa zea, a major pest of over 300 crops, was evaluated using CRISPR/Cas9 to progressively eliminate different functional ABCC2 domains. Results from bioassays with edited insect lines support that muta-tions in ABCC2 was associated with Cry1Ac resistance ratios (RR) ranging from 7.3- to 39.8-fold. No significant differences in susceptibility to Cry1Ac were detected between H. zea with partial or complete ABCC2 knockout, although highest levels of tolerance were observed when knocking out half of ABCC2. Based on >500-1,000-fold RRs reported in similar studies for closely related moth species, the low RRs observed in H. zea knockouts support that ABCC2 is not a major Cry1Ac receptor in this insect.

Insecticidal toxins from Bacillus thuringiensis (Bt) are valuable tools for pest management worldwide, contributing to the management of human disease insect vectors and phytophagous insect pests of agriculture and forestry. Here, we report the effects of dual and triple Bt toxins expressed in transgenic cotton cultivars on the fitness and demographic performance of Helicoverpa zea (Boddie), a noctuid pest known as cotton bollworm and corn earworm. Life-history traits were determined for individuals of three field populations from a region where H. zea overwintering is likely. Triple-gene Bt cotton cultivars expressing Cry and Vip3Aa toxins killed 100% of the larvae in all populations tested. In contrast, dual-gene Bt cotton expressing Cry1Ac+Cry1F and Cry1Ac+Cry2Ab2 allowed population growth with the intrinsic rate of population growth (rm) 38% lower than on non-Bt cotton. The insects feeding on Bt cotton plants expressing Cry1Ac+Cry2Ab2, Cry1Ac+Cry1F, or Cry1Ab+Cry2Ae exhibited reduced larval weight, survival rate, and increased development time. Additionally, fitness parameters varied significantly among the insect populations, even on non-Bt cotton plants, likely because of their different genetic background and/or previous Bt toxin exposure. This is the first report of the comparative fitness of H. zea field populations on dual-gene Bt cotton after the recent reports of field resistance to certain Bt toxins. These results document the population growth rates of H. zea from an agricultural landscape with 100% Bt cotton cultivars. Our results will help to refine models designed to predict resistance evolution and improve insect resistance management for Bt crops.