Mortality of the cotton boll weevil in drip and sprinkler irrigated cotton crops

The cotton boll weevil, Anthonomus grandis grandis Boheman (Coleoptera: Curculionidae), is a key cotton crop pest in Brazil. Adverse climatic factors, such as high temperatures and low soil moisture, dehydrate oviposited cotton squares (bud flowers) on the ground and cause high mortality of its offspring within these plant structures. The objective of this research was to evaluate the mortality of the cotton boll weevil in drip and sprinkler irrigated cotton crops. The experimental design was in randomized blocks with two treatments: drip (T1) and sprinkler (T2, control) irrigated cotton crops with sixteen replications. Each parcel had one emergence cage, installed between two cotton rows per irrigation system, with 37 cotton squares with opened oviposition punctures and yellowish bracts, to capture adult cotton boll weevils. The average number of boll weevils that emerged from the cotton squares and the causes of mortality at different development stages were determined per treatment. Third-generation life tables of the boll weevil were prepared using the natural mortality data in drip and sprinkler irrigation treatments and plus actual, apparent and indispensable mortality rates and the lethality of each mortality cause. We conclude that the application of water directly to the root zone of the plants in a targeted manner, using the drip irrigation system, can cause high mortality of the cotton boll weevil immature stages inside cotton squares fallen on the ground. This is because the cotton squares fallen on the drier and hotter soil between the rows of drip-irrigated cotton dehydrates causing the boll weevils to die. This is important because it can reduce its population density of the pest and, consequently, the number of applications of chemical insecticides for its control. Thus, contributing to increase the viability of cotton production, mainly in areas of the Brazilian semiarid region where the cotton is cultivated in organic system.

Yield Estimation of High-Density Cotton Fields Using Low-Altitude UAV Imaging and Deep Learning

Background: Different from other parts of the world, China has its own cotton planting pattern. Cotton are densely planted in wide-narrow rows to increase yield in Xinjiang, China, causing the difficulty in the accurate evaluation of cotton yields using remote sensing in such field with branches occluded and overlapped. Results: In this study, low-altitude unmanned aerial vehicle (UAV) imaging and deep convolutional neural networks (DCNN) were used to estimate the yields of densely planted cotton. Images of cotton field were acquired by an UAV at the height of 5 m. Cotton bolls were manually harvested and weighted afterwards. Then, a modified DCNN model was developed by applying encoder-decoder recombination and dilated convolution for pixel-wise cotton boll segmentation termed CD-SegNet. Linear regression analysis was employed to build up the relationship between cotton boll pixels ratio and cotton yield. Yield estimations of four cotton fields were verified after machine harvest and weighting. The results showed that CD-SegNet outperformed the other tested models including SegNet, support vector machine (SVM), and random forest (RF). The average error of the estimated yield of the cotton fields was 6.2%. Conclusions: Overall, the yield estimation of densely planted cotton based on lowaltitude UAV imaging is feasible. This study provides a methodological reference for cotton yield estimation in China.

An improved YOLO network for unopened cotton boll detection in the field

Detection of cotton bolls in the field environments is one of crucial techniques for many precision agriculture applications, including yield estimation, disease and pest recognition and automatic harvesting. Because of the complex conditions, such as different growth periods and occlusion among leaves and bolls, detection in the field environments is a task with considerable challenges. Despite this, the development of deep learning technologies have shown great potential to effectively solve this task. In this work, we propose an Improved YOLOv5 network to detect unopened cotton bolls in the field accurately and with lower cost, which combines DenseNet, attention mechanism and Bi-FPN. Besides, we modify the architecture of the network to get larger feature maps from shallower network layers to enhance the ability of detecting bolls due to the size of cotton boll is generally small. We collect image data of cotton in Aodu Farm in Xinjiang Province, China and establish a dataset containing 616 high-resolution images. The experiment results show that the proposed method is superior to the original YOLOv5 model and other methods such as YOLOv3,SSD and FasterRCNN considering the detection accuracy, computational cost, model size and speed at the same time. The detection of cotton boll can be further applied for different purposes such as yield prediction and identification of diseases and pests in earlier stage which can effectively help farmers take effective approaches in time and reduce the crop losses and therefore increase production.

VARIABILITY OF MORPHO-BIOLOGICAL TRAITS UNDER THE INFLUENCE OF LOW-FREQUENCY ELECTROMAGNETIC FIELDS ON VEGETATIVE ORGANS OF COTTON

In this article presents the obtained data on research the effect of low-frequency electromagnetic fields on the vegetative organs of cotton in conditions of normal and insufficient water regime. On the basis of the obtained results was revealed that the treatment of the vegetative organs of cotton with EMF will accelerate the growth-development, ripeness and resistance to water deficiency. KEY WORDS: cotton, processing, vegetative organs, low-frequency electromagnetic fields, water supply, plant height, between nodes, cotton boll.