GIS-based volunteer cotton habitat prediction and plant-level detection with UAV remote sensing

Volunteer cotton plants germinate and grow at unwanted locations like transport routes and can serve as hosts for a harmful cotton pests called cotton boll weevils. The main objective of this study was to develop a geographic information system (GIS) framework to efficiently locate volunteer cotton plants in the cotton production regions in southern Texas, thus reducing time and economic cost for their removal. A GIS network analysis tool was applied to estimate the most likely routes for cotton transportation, and a GIS model was created to identify and visualize potential areas of volunteer cotton growth. The GIS model indicated that, of the 31 counties in southern Texas that may have habitat for volunteer cotton, Hidalgo, Cameron, Nueces, and San Patricio are the counties at the greatest risk. Moreover, a method based on unmanned aerial vehicle (UAV) remote sensing was proposed to detect the precise locations of volunteer cotton plants in potential areas for their subsequent removal. In this study, a UAV was used to scan limited samples of potential volunteer cotton growth areas identified with the GIS model. The results indicated that UAV remote sensing coupled with the proposed image analysis methods could accurately identify the precise locations of volunteer cotton and could potentially assist in the elimination of volunteer cotton along transport routes

Detecting Volunteer Cotton Plants in a Corn Field with Deep Learning on UAV Remote-Sensing Imagery

The cotton boll weevil, Anthonomus grandis Boheman is a serious pest to the U.S. cotton industry that has cost more than 16 billion USD in damages since it entered the United States from Mexico in the late 1800s. This pest has been nearly eradicated; however, southern part of Texas still faces this issue and is always prone to the pest reinfestation each year due to its sub-tropical climate where cotton plants can grow year-round. Volunteer cotton (VC) plants growing in the fields of inter-seasonal crops, like corn, can serve as hosts to these pests once they reach pin-head square stage (5-6 leaf stage) and therefore need to be detected, located, and destroyed or sprayed . In this paper, we present a study to detect VC plants in a corn field using YOLOv3 on three band aerial images collected by unmanned aircraft system (UAS). The two-fold objectives of this paper were : (i) to determine whether YOLOv3 can be used for VC detection in a corn field using RGB (red, green, and blue) aerial images collected by UAS and (ii) to investigate the behavior of YOLOv3 on images at three different scales (320 x 320, S1; 416 x 416, S2; and 512 x 512, S3 pixels) based on average precision (AP), mean average precision (mAP) and F1-score at 95% confidence level. No significant differences existed for mAP among the three scales, while a significant difference was found for AP between S1 and S3 (p = 0.04) and S2 and S3 (p = 0.02). A significant difference was also found for F1-score between S2 and S3 (p = 0.02). The lack of significant differences of mAP at all the three scales indicated that the trained YOLOv3 model can be used on a computer vision-based remotely piloted aerial application system (RPAAS) for VC detection and spray application in near real-time.Comment: 38 Page

Computer Vision for Volunteer Cotton Detection in a Corn Field with UAS Remote Sensing Imagery and Spot Spray Applications

To control boll weevil (Anthonomus grandis L.) pest re-infestation in cotton fields, the current practices of volunteer cotton (VC) (Gossypium hirsutum L.) plant detection in fields of rotation crops like corn (Zea mays L.) and sorghum (Sorghum bicolor L.) involve manual field scouting at the edges of fields. This leads to many VC plants growing in the middle of fields remain undetected that continue to grow side by side along with corn and sorghum. When they reach pinhead squaring stage (5-6 leaves), they can serve as hosts for the boll weevil pests. Therefore, it is required to detect, locate and then precisely spot-spray them with chemicals. In this paper, we present the application of YOLOv5m on radiometrically and gamma-corrected low resolution (1.2 Megapixel) multispectral imagery for detecting and locating VC plants growing in the middle of tasseling (VT) growth stage of cornfield. Our results show that VC plants can be detected with a mean average precision (mAP) of 79% and classification accuracy of 78% on images of size 1207 x 923 pixels at an average inference speed of nearly 47 frames per second (FPS) on NVIDIA Tesla P100 GPU-16GB and 0.4 FPS on NVIDIA Jetson TX2 GPU. We also demonstrate the application of a customized unmanned aircraft systems (UAS) for spot-spray applications based on the developed computer vision (CV) algorithm and how it can be used for near real-time detection and mitigation of VC plants growing in corn fields for efficient management of the boll weevil pests.Comment: 39 page

Approaches in biotechnological applications of natural polymers

Natural polymers, such as gums and mucilage, are biocompatible, cheap, easily available and non-toxic materials of native origin. These polymers are increasingly preferred over synthetic materials for industrial applications due to their intrinsic properties, as well as they are considered alternative sources of raw materials since they present characteristics of sustainability, biodegradability and biosafety. As definition, gums and mucilages are polysaccharides or complex carbohydrates consisting of one or more monosaccharides or their derivatives linked in bewildering variety of linkages and structures. Natural gums are considered polysaccharides naturally occurring in varieties of plant seeds and exudates, tree or shrub exudates, seaweed extracts, fungi, bacteria, and animal sources. Water-soluble gums, also known as hydrocolloids, are considered exudates and are pathological products; therefore, they do not form a part of cell wall. On the other hand, mucilages are part of cell and physiological products. It is important to highlight that gums represent the largest amounts of polymer materials derived from plants. Gums have enormously large and broad applications in both food and non-food industries, being commonly used as thickening, binding, emulsifying, suspending, stabilizing agents and matrices for drug release in pharmaceutical and cosmetic industries. In the food industry, their gelling properties and the ability to mold edible films and coatings are extensively studied. The use of gums depends on the intrinsic properties that they provide, often at costs below those of synthetic polymers. For upgrading the value of gums, they are being processed into various forms, including the most recent nanomaterials, for various biotechnological applications. Thus, the main natural polymers including galactomannans, cellulose, chitin, agar, carrageenan, alginate, cashew gum, pectin and starch, in addition to the current researches about them are reviewed in this article.. }To the Conselho Nacional de Desenvolvimento Cientfíico e Tecnológico (CNPq) for fellowships (LCBBC and MGCC) and the Coordenação de Aperfeiçoamento de Pessoal de Nvíel Superior (CAPES) (PBSA). This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit, the Project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462) and COMPETE 2020 (POCI-01-0145-FEDER-006684) (JAT)

Artificial Intelligence-Based Computer Vision for Rapid Detection and Classification of Objects in Agricultural Situations

In the past few years, computer vision (CV) has made significant progress due to successive improvements in computing hardware alongside machine learning (ML) and deep learning (DL) algorithms. The progressive improvements in ML and DL algorithms have enabled artificial intelligence (AI)-based CV applications. However, the agriculture sector has lagged in harnessing the immense potential of AI-based CV. Therefore, through this thesis work, applications of different AI-based CV algorithms in solving three major challenges faced by the U.S. cotton industry (detecting volunteer cotton (VC) plants in corn fields for spot-spray applications, detecting plastic shopping bags in cotton fields and classifying images of cotton leaf grades) have been presented. VC plants growing in the middle of inter-seasonal crops like corn and sorghum can act as hosts for boll weevil pests and therefore they need to be detected, located, and sprayed. Both YOLOv3 and YOLOv5 were used to detect and locate VC plants in corn fields at three different growth stages (V3, V6 and VT) using aerial remote sensing imagery. Both the algorithms were able to detect the VC plants at accuracies greater than 90%; however, due to faster inference speed, YOLOv5 is recommended during the V3 growth stage of corn plants for near to real-time detection. The GPS coordinates of detected VC plants were used to generate optimal flight path using ant colony optimization algorithm for spot-spray applications with unmanned aircraft systems. Plastic contamination in cotton is a serious and prevalent issue that incurs an annual loss of more than 750 million USD to the U.S. cotton industry. One of the many sources of plastic contaminants is plastic shopping bags getting carried away by wind and then tangling on cotton plants. These bags get mixed with cotton fibers during harvest and pose problems at cotton gins beside contaminating the fibers and reducing its grade. Therefore, they need to be detected and located before harvest to minimize the amount of contamination that may end up at cotton gins. It was found that YOLOv5 could detect white and brown color bags with accuracies of 92% and 85% respectively. YOLOv5m was found to be the most desirable variant among the four (others being YOLOv5s, YOLOv5l and YOLOv5x) keeping the mAP above 90% and average inference speed of about 86 frames per second. In the final study of this thesis, a custom VGG16 network was used with softmax and support vector machine (SVM) classifier for classifying images belonging to five cotton leaf grades (i.e., grades 2, 3, 4, 5 and 6). It was found that SVM in the custom VGG16 network could achieve the same accuracy as softmax classifier but at a much smaller computation time

Experimental Study on Hot Bituminous Mix

About 80% of paved roads in India are made of flexible pavement, which is made by heating and mixing aggregates and asphalt binders, so warm-mix asphalt is becoming more prevalent these days. Hot mix asphalt typically has a mixing temperature in the range of 100 to 135 °C (Hurley and Prowell, 2005), whereas hot mix asphalt has a mixing temperature of 150 to 180 °C (300 to 350 °F). WMA uses chemical and organic additives and foaming technology to produce asphalt mixes at low temperatures by reducing binder viscosity, making the mix workable without affecting asphalt performance. Energy consumption, global warming, asphalt oxidation hardening, and the total cost of the asphalt industry are reduced by warm-mix asphalt while also creating a better working environment. WMA is produced, placed and compacted at temperatures 10°C to 40°C lower than control hot-mix asphalts (D`Angelo et al., 2008). However, the low blending temperature raises concerns about blend performance. Therefore, WMA blends should be thoroughly evaluated and characterized to ensure adequate performance

Suitable Integrated Approach for Management of Fusarium Wilt of Tomato caused by Fusarium oxysporum f. sp. lycopersici (Sacc.)

Among the different integrated approaches for management of Fusarium wilt and their effect on growth and yield parameters of tomato revealed that soil application of FYM @ 100gm/pot + Neem cake @ 100gm/pot + seedling treatment with bio-formulation of Azotobater @ 5% + foliar spray of Carbendazim @ 0.1% was showing minimum disease incidence with 6.23, 10.11 and 15.03 per cent at 7, 14 and 21 days after inoculation, respectively. The observations on plant height of tomato was found in T3 treatment representing the value 17.00, 18.85, 20.66, 22.10, 24.10 and 27.30 cm at 5, 10, 15, 20, 25 and 30 days age of seedling, respectively against the minimum plant height i.e. 10.42, 10.92, 11.56, 11.76, and 12.55 in case of control (T10). The effect of integrated approach on branching of shoot in tomato was estimated at 85 days age of plant which revealed the maximum number of branch with 5.00 was found in case of soil application of FYM @ 100gm/pot + Neem cake@ 100gm/pot + seedling treatment with bio-formulation of Azotobater @ 5% + foliar spray of Carbendazim @ 0.1% whereas, in case of control it was only 2.33. The morphological character of roots was examined and recorded developed robust root system in T3 treatment while the less fibrous, weakly developed roots in control. The maximum yield was recorded per plant in T3 treatment (soil application of FYM @ 100gm/pot + Neem cake @ 100gm/pot + seedling treatment with bio-formulation of Azotobator @ 5% +foliar spray of Carbendazim @0.1%.) represented the value 490.30g per plant. Similarly, the maximum large size tuber with 4 in number was recorded in treatment T3 (soil application of FYM@ 100gm/pot+ Neem cake@ 100gm/pot + seedling treatment with bio-formulation of Azotobator@5% +foliar spray of Carbendazim @ 0.1%) followed by treatment T9 (Soil application of FYM @ 100 gm/pot + Neem cake @ 100 gm +seedling treatment with bio-formulation of T. viride + foliar spray Carbendazim @0.1%) as 03

Bacteriological profile of neonatal sepsis and antibiotic susceptibility pattern of isolates admitted at Kanti Children’s Hospital, Kathmandu, Nepal

Abstract Objective Neonatal sepsis is a major cause of morbidity and mortality of newborns (< 1 month of age). Septicemia and drug resistance is a predominant issue for neonatal death in Nepal. This study is intended to find bacteriological profile of neonatal sepsis and antibiotic susceptibility pattern of the isolates from neonates at Kanti Children’s Hospital, Kathmandu, Nepal. Results Out of 350 suspected cases of neonatal sepsis, 59 (16.9%) cases showed positive blood culture. The prevalent of positive blood culture with different neonatal risk factors (sex, age, birth weight, gestational age, and delivery mode) showed highest positive bacterial growth in male (52.3%); 3 or above 3 days age (71.2%); low birth weight (62.7%); preterm gestational age (31.4%); and caesarean delivery mode (63.3%). Among positive cases, the bacteriological profile was found highest for Staphylococcus aureus (35.6%) followed by Klebsiella pneumoniae (15.3%). The most sensitive and resistive antibiotics among Gram-positive isolates were gentamicin (93%) and ampicillin (78%), respectively. Meropenem and imipenem showed highest 100% effective and cefotaxime was least (28%) sensitive among Gram-negative isolates. This concludes broad ranges of bacteria are associated with neonatal sepsis and revealed variation in antibiotic susceptibility pattern among bacterial isolates

Assessing the performance of YOLOv5 algorithm for detecting volunteer cotton plants in corn fields at three different growth stages

The feral or volunteer cotton (VC) plants when reach the pinhead squaring phase (5–6 leaf stage) can act as hosts for the boll weevil (Anthonomus grandis L.) pests. The Texas Boll Weevil Eradication Program (TBWEP) employs people to locate and eliminate VC plants growing by the side of roads or fields with rotation crops but the ones growing in the middle of fields remain undetected. In this paper, we demonstrate the application of computer vision (CV) algorithm based on You Only Look Once version 5 (YOLOv5) for detecting VC plants growing in the middle of corn fields at three different growth stages (V3, V6 and VT) using unmanned aircraft systems (UAS) remote sensing imagery. All the four variants of YOLOv5 (s, m, l, and x) were used and their performances were compared based on classification accuracy, mean average precision (mAP) and F1-score. It was found that YOLOv5s could detect VC plants with maximum classification accuracy of 98% and mAP of 96.3% at V6 stage of corn while YOLOv5s and YOLOv5m resulted in the lowest classification accuracy of 85% and YOLOv5m and YOLOv5l had the least mAP of 86.5% at VT stage on images of size 416 × 416 pixels. The developed CV algorithm has the potential to effectively detect and locate VC plants growing in the middle of corn fields as well as expedite the management aspects of TBWEP