Pesticide Spraying Research Articles

Smart Grass Cutter and Pesticide Sprayer Robot with Mobile Control

Smart Grass Cutter and Pesticide Sprayer Robot with Mobile Control

A fundamental analysis on PAHs in food products to detect the toxicity index using fuzzy logic system

Polycyclic Aromatic Hydrocarbons (PAHs) are complex chemical compounds that occur naturally in unprocessed food when it is exposed to contaminated air during transportation, natural emission such as volcano, forest fire and through pesticides spray. It is reported by different agencies that there are 16 types of PAHs in which BaP (Benzo[a] pyrene), BaA (Benz[a]anthracene), BbF(Benzo [b] fluoranthene), Chr (Chrysene) are considered to be carcinogenic and it can occur due to different processes. In processed food it occurs due to various processing methods like overheating, incomplete burning, drying etc. The presence of PAH in food is conventionally found through analytical, traditional, and semi-automatic methods. These methods are found to be valuable but expensive and time-consuming. Further, these methods are used only for the detection of PAHs and the toxicity level is measured or identified based on expert knowledge of researchers and the Standards. Therefore, in this research, a simple harmfulness index system has been developed using Fuzzy Logic System(FLS). The proposed system has been designed based on the PAH values of different food and food products. Hence to initiate the study and to determine the significance of the results, PAH data have been collected from different articles that have investigated food products experimentally. These PAH data were analyzed using statistical measures such as Min, Mean, Max, Standard Deviation, Variance and Kurtosis method. Based on the observations from the results, the fuzzy sets were designed with four membership functions for each PAH and the rules were framed. The strength output from the inference engine has been associated with harmfulness index such as normal, low risk, medium risk, and high risk. From the evaluation, it can be observed that 89.72% of the food samples were recognized along with their degree of harmfulness. Also it can be inferred that 11% of the misclassified samples showed clear metrics of their harmfulness with PAH variations.

Knowledge, Attitude and Practices of Pesticides Use and Serum Cholinesterase Level Among Farm Workers in Bhaktapur – A Cross Sectional Study

Abstract Introduction/Objective Pesticides use has been an integral part of modern agricultural practices. Lack of knowledge and unsafe handling of pesticides uses have kept the farm workers in the frontline of vulnerability. Serum cholinesterase show a dose-exposure relationship with organophosphorus. This study aims to identify the existing knowledge, attitude and practices of pesticides use among farm workers and to measure the serum cholinesterase level before and after spraying of the pesticides. Methods/Case Report This is a cross-sectional study carried out among farm workers of Madhyapur Municipality, Bhaktapur, Nepal. Ethical clearance was obtained from Nepal Health Research Council, Nepal. House hold survey was carried out for the knowledge, attitude and practices of pesticides use using structured questionnaires. Written consent was taken. Blood was collected twice, before and after spraying season for 97 farm workers. Serum cholinesterase was analyzed in the vitros 4600 biochemistry analyzer. Paired t-tests and chi square tests were used for statistical analysis. Results (if a Case Study enter NA) The mean cholinesterase of farm workers before and after pesticide spraying was 6.92±1.47 U/mL and 6.49±1.47 U/mL respectively, with p value of <0.001. There were also significant differences of serum cholinesterase level among farm workers before and after pesticide spraying on the basis of education level, type of involvement either full time or part time and prior integrated pest management training. This study found that 63% of respondents had good knowledge on pesticides while 25% had average and 12% had poor knowledge. Similarly, this study also found that 54% of respondents had good knowledge on pesticides use while 26% had average and 20% had poor knowledge. However, this study showed that only 44% of farm workers had good practice. Association between knowledge of pesticide and practices and between knowledge of pesticides use and practices were statistically significant with p-value of 0.001 and 0.001 respectively. Conclusion There is a significant difference in the level of serum cholinesterase before and after spraying pesticides in farm workers. Lack of education, lack of integrated pest management training and poor practices of pesticides use are the reasons behind the difference in the serum cholinesterase level.

Design of Integrated Dual Spiral Spring and Rotary Hydraulic Damper System for Torsional Vibration Damping in Pesticide Sprayer Gearbox

Design of Integrated Dual Spiral Spring and Rotary Hydraulic Damper System for Torsional Vibration Damping in Pesticide Sprayer Gearbox

IoT Based Precision Farming

This project focuses on leveraging drone images of the pests equipped with advanced sensors for pest detection in crops, combined with methods for image processing to identify diseases. The ultimate goal is to enhance crop health and productivity through timely and targeted pesticide application. Image processing techniques are used to detect signs of diseases and pests in the captured images. The use of machine learning CNN algorithm enhances the system’s ability to accurately classify and diagnose crop heath issues. Upon detection of pests, the IOT platform triggers a response mechanism to deploy a precision pesticide spraying system. This ensures targeted and localized treatment, reducing the overall use of pesticides and minimizing environmental impact. This project involves capturing images of pests using a camera, followed by processing these images to extract key features using various image processing techniques. The extracted features are analyzed using algorithms, primarily Convolutional Neural Networks (CNNs), to detect variations in color and other dominant characteristics in the images. By comparing these features across samples, the system can identify pests and plant diseases more efficiently. This approach aims to provide a quicker and more cost-effective solution for pest detection and disease management.

Computational fluid dynamics investigation of pesticide spraying by agricultural drones

Precise control over pesticide spraying by agricultural drones requires fundamental research of multiphase flow, heat and mass transfer of liquid droplets in downwash flow. In this study, water was used as a carrier agent in pesticide formulations, in which the liquid droplets exhibit a Newtonian fluid behavior. A general computational fluid dynamics (CFD) model that characterizes propeller motion coupled with droplet evaporation and deposition was developed. The downwash flow generated by the propeller rotation was simulated using a multiple reference frame method along with the SST-k-ω turbulence model. Spraying of liquid droplets was simulated using a discrete phase model. The flow model validation was conducted by comparing the simulated velocities with experimental data, and the spray model validation was completed by a comparison of droplet depositions from CFD predictions and experiments. The results indicated that the droplet deposition fluctuates with an increase of propeller speed, and that both temperature and relative humidity affect the droplet deposition. In addition, the relation between the number of particles and the impact velocity was identified quantitatively, impact velocity with respect to particle diameter was predicted, and the effects of surface inclination, surface roughness, and crosswind speed on droplet deposition were analyzed. Moreover, an in-depth discussion about the intrinsic relation between downwash flow and droplet spraying was conducted.

Evaluations of drone based pesticide spraying effects on pollinators and natural enemies in brinjal (Solanum melongena L.) ecosystem

Evaluations of drone based pesticide spraying effects on pollinators and natural enemies in brinjal (Solanum melongena L.) ecosystem

Airflow Modeling for Citrus under Protective Screens

This study explores the development and validation of an airflow model to support climate prediction for Citrus Under Protective Screens (CUPS) in California. CUPS is a permeable screen structure designed to protect a field of citrus trees from large insects including the vector that causes the devastating citrus greening disease. Because screen structures modify the environmental conditions (e.g., temperature, relative humidity, airflow), farm management and treatment strategies (e.g., pesticide spraying events) must be modified to account for these differences. Toward this end, we develop a model for predicting wind speed and direction in a commercial-scale research CUPS, using a computational fluid dynamics (CFD) model. We describe the model and validate it in two ways. In the first, we model a small-scale replica CUPS under controlled conditions and compare modeled and measured airflow in and around the replica structure. In the second, we model the full-scale CUPS and use historical measurements to “back test” the model’s accuracy. In both settings, the modeled airflow values fall within statistical confidence intervals generated from the corresponding measurements of the conditions being modeled. These findings suggest that the model can aid decision support and smart agriculture solutions for farmers as they adapt their farm management practices for CUPS structures.

Harnessing UAVs and deep learning for accurate grass weed detection in wheat fields: a study on biomass and yield implications

Weeds are undesired plants competing with crops for light, nutrients, and water, negatively impacting crop growth. Identifying weeds in wheat fields accurately is important for precise pesticide spraying and targeted weed control. Grass weeds in their early growth stages look very similar to wheat seedlings, making them difficult to identify. In this study, we focused on wheat fields with varying levels of grass weed infestation and used unmanned aerial vehicles (UAVs) to obtain images. By utilizing deep learning algorithms and spectral analysis technology, the weeds were identified and extracted accurately from wheat fields. Our results showed that the precision of weed detection in scattered wheat fields was 91.27% and 87.51% in drilled wheat fields. Compared to areas without weeds, the increase in weed density led to a decrease in wheat biomass, with the maximum biomass decreasing by 71%. The effect of weed density on yield was similar, with the maximum yield decreasing by 4320 kg·ha− 1, a drop of 60%. In this study, a method for monitoring weed occurrence in wheat fields was established, and the effects of weeds on wheat growth in different growth periods and weed densities were studied by accurately extracting weeds from wheat fields. The results can provide a reference for weed control and hazard assessment research.

Evaluation of droplet deposition parameters based on the Genetic-Otsu algorithm.

Pesticide spraying is a cost-effective way to control crop pests and diseases. The effectiveness of this method relies on the deposition and distribution of the spray droplets within the targeted application area. There is a critical need for an accurate and stable detection algorithm to evaluate the liquid droplet deposition parameters on the water-sensitive paper (WSP) and reduce the impact of image noise. This study acquired 90 WSP samples with diverse coverage through field spraying experiments. The droplets on the WSP were subsequently isolated, and the coverage and density were computed, employing the fixed threshold method, the Otsu threshold method, and our Genetic-Otsu threshold method. Based on the benchmark of manually measured data, an error analysis was conducted on the accuracy of three methods, and a comprehensive evaluation was carried out. The relative error results indicate that the Genetic-Otsu method proposed in this research demonstrates superior performance in detecting droplet coverage and density. The relative errors of droplet density in the sparse, medium, and dense droplet groups are 2.7%, 1.5%, and 2.0%, respectively. The relative errors of droplet coverage are 1.5%, 0.88%, and 1.2%, respectively. These results demonstrate that the Genetic-Otsu algorithm outperforms the other two algorithms. The proposed algorithm effectively identifies small-sized droplets and accurately distinguishes the multiple independent contours of adjacent droplets even in dense droplet groups, demonstrating excellent performance. Overall, the Genetic-Otsu algorithm offered a reliable solution for detecting droplet deposition parameters on WSP, providing an efficient tool for evaluating droplet deposition parameters in UAV pesticide spraying applications.

Automatic Disease Detection from Strawberry Leaf Based on Improved YOLOv8.

Strawberries are susceptible to various diseases during their growth, and leaves may show signs of diseases as a response. Given that these diseases generate yield loss and compromise the quality of strawberries, timely detection is imperative. To automatically identify diseases in strawberry leaves, a KTD-YOLOv8 model is introduced to enhance both accuracy and speed. The KernelWarehouse convolution is employed to replace the traditional component in the backbone of the YOLOv8 to reduce the computational complexity. In addition, the Triplet Attention mechanism is added to fully extract and fuse multi-scale features. Furthermore, a parameter-sharing diverse branch block (DBB) sharing head is constructed to improve the model's target processing ability at different spatial scales and increase its accuracy without adding too much calculation. The experimental results show that, compared with the original YOLOv8, the proposed KTD-YOLOv8 increases the average accuracy by 2.8% and reduces the floating-point calculation by 38.5%. It provides a new option to guide the intelligent plant monitoring system and precision pesticide spraying system during the growth of strawberry plants.

Classification of infection grade for anthracnose in mango leaves under complex background based on CBAM-DBIRNet

Anthracnose is one of the most harmful fungal diseases in mango orchards. Accurate and rapid detection of anthracnose infection grade for mango leaves in complex natural environments is a prerequisite of precise variable pesticide spraying by robots in intelligent orchards. In this paper, A lightweight double branch inverted residual attention network (CBAM-DBIRNet) model was proposed by combining the inverted residual network and the attention mechanism, which was used to detect the infection grade of anthracnose for mango leaves in natural environment. The model designed two parallel residual branch structures to extract multi-scale features of mango leaves with different infection grades through different dilation coefficients. The inverted residual structure and depthwise separable convolution were used to reduce the complexity of the model. The CBAM attention module was integrated into the model to suppress the interference of complex background information. The mango leaves collected in the natural orchard were used for experiments. The results showed that the accuracy, recall and F1 of the CBAM-DBIRNet model for the classification of anthracnose infection grade were 98.42 %, 97.80 % and 97.89 %, respectively. The model size and parameter were 0.64 MB and 0.15 M, respectively. This study provides technical support for smart orchard management and robotic precision variable application operations.

IoT-based Automatic Pesticide Sprayer Control System on Guava Trees

IoT-based Automatic Pesticide Sprayer Control System on Guava Trees

EFS-Former: An Efficient Network for Fruit Tree Leaf Disease Segmentation and Severity Assessment

Fruit is a major source of vitamins, minerals, and dietary fiber in people’s daily lives. Leaf diseases caused by climate change and other factors have significantly reduced fruit production. Deep learning methods for segmenting leaf diseases can effectively mitigate this issue. However, challenges such as leaf folding, jaggedness, and light shading make edge feature extraction difficult, affecting segmentation accuracy. To address these problems, this paper proposes a method based on EFS-Former. The expanded local detail (ELD) module extends the model’s receptive field by expanding the convolution, better handling fine spots and effectively reducing information loss. H-attention reduces computational redundancy by superimposing multi-layer convolutions, significantly improving feature filtering. The parallel fusion architecture effectively utilizes the different feature extraction intervals of the convolutional neural network (CNN) and Transformer encoders, achieving comprehensive feature extraction and effectively fusing detailed and semantic information in the channel and spatial dimensions within the feature fusion module (FFM). Experiments show that, compared to DeepLabV3+, this method achieves 10.78%, 9.51%, 0.72%, and 8.00% higher scores for mean intersection over union (mIoU), mean pixel accuracy (mPA), accuracy (Acc), and F_score, respectively, while having 1.78 M fewer total parameters and 0.32 G lower floating point operations per second (FLOPS). Additionally, it effectively calculates the ratio of leaf area occupied by spots. This method is also effective in calculating the disease period by analyzing the ratio of leaf area occupied by diseased spots. The method’s overall performance is evaluated using mIoU, mPA, Acc, and F_score metrics, achieving 88.60%, 93.49%, 98.60%, and 95.90%, respectively. In summary, this study offers an efficient and accurate method for fruit tree leaf spot segmentation, providing a solid foundation for the precise analysis of fruit tree leaves and spots, and supporting smart agriculture for precision pesticide spraying.

Regulation mechanism of droplets wetting on banana leaf surface and its dynamic contact angle wetting model

The effective wetting and deposition of pesticide droplets on the leaf surface significantly affect the control of pests and diseases, and its mainly affected by the leaf surface properties and pesticide formulation properties. This article aims to investigate the wettability regulation mechanism and dynamic wetting properties of banana leaves, in order to intelligently design appropriate pesticide formulations. The micro-morphology of the banana leaves exhibits a micro-nano dual-scale structure with different microstructures and roughness on the adaxial side and abaxial side. Correspondingly, the wettability on the adaxial side of the banana leaf with higher roughness (Rq = 71.1 nm) is always better than that on the abaxial side (Rq = 42.5 nm). The droplet contact performance of droplets exhibits a strong concentration dependence of surfactants, which regulate the inter-species differences in the wettability of different pesticide formulations on both sides. Besides, the dynamic wetting process of pesticide droplets exhibits strong time dependence, by using a time series method to establish an AR(2) model, precise simulation and prediction of the contact angle changes on the adaxial side (R2 = 0.9560) and abaxial side (R2 = 0.8903) during the dynamic wetting process were achieved. The dynamic wetting model provides new insights into the spreading and deposition processes of droplets on leaves. This work provides a favorable reference for the study of the dynamic balance between wetting and adhesion properties of pesticide spray on leaf surfaces.

Pollinator visitation decline due to pesticide application beyond threshold frequency brings down crop yield

Abstract The deleterious effects of pesticides on pollinating insect abundance are well established but the relationship among pesticide application, pollinator decline and the corresponding change in marketable yield is not fully understood. In this study, we assessed what level of pesticide exposure causes maximum pollinator loss and at that level of pollinator loss if there is any crop yield loss. We primarily assessed pollinator crop flower visitation in 27 aubergine farms with different degrees of pesticide application. Subsequently, we also observed pollinator activity and aubergine yield (fruit set) in a semi‐controlled field experiment with four different pesticide treatments. Pesticide application frequency was the most important driver of pollinator visitation compared with quantity. Pesticide spray above once a week caused a drastic drop in pollinator visitation in a non‐linear fashion showing a threshold of pesticide application. In high pesticide frequency experimental plots, pollinator visitation was significantly lower than in control, low and medium frequency plots. This resulted in lowering of aubergine fruit set by 27.4% in high pesticide frequency plots. Use of synthetic pesticides to safeguard yield seems to be doing just the opposite by bringing down yield from pesticide‐induced pollinator limitation.

Mild Cognitive Impairment Associated with Pesticides use Among Vegetable Farmers and Their Wives in Sukorambi Village Jember Regency

Introduction: Mild Cognitive Impairment (MCI), according to several studies, has been discovered to be related to exposure to pesticides. Sukorambi Village is the largest vegetable producer village in Jember Regency and pesticides are used in the vegetable cultivation process. This study aimed to analyze the relationship between the age of vegetable farmers and the frequency of pesticide spraying with the incidence of MCI and to analyze differences in the incidence of MCI in farmers and their wives. Methods: This research is an analytical research with a cross-sectional design which was carried out in Sukorambi Village, from June to December 2022. The samples of this research are 142 people, obtained from a proportional stratified random sampling technique and represented groups of farmers in each hamlet. In this case, the research variables include age, frequency of pesticide spraying, as well as the incidence of MCI in vegetable farmers and their wives. Data were further collected through direct interviews, which were then analyzed through bivariate analysis using Spearman and paired t tests. Results: The results showed that the majority of vegetable farmers were above 55 years old and most of them sprayed pesticides for 3-4 and 5-6 times a month. Most farmers and their wives experience MCI. Conclusion: The farmer's age and the frequency of spraying pesticides are related to the incidence of MCI in farmers. There is a significant difference between the incidence of MCI in vegetable farmers and their wives, where MCI is more experienced by vegetable farmer wives.

A multimodal sensing network based on synergistically sensitized polyaniline composites strategy for safety monitoring in pesticide spraying

Ensuring the safety of farmers during pesticide spraying is crucial in smart agricultural practices due to the potential risks of pesticide poison and the associated irreversible damages. However, there is a notable deficiency of efficient monitoring strategies for providing timely and cost-effective safety warnings. In this study, we have devised an advanced and real-time sensing network using polyaniline (PANI) composites to address this pressing concern. To enhance sensing performance, diverse quantum dots (QDs) sensitizers and rigid or flexible substrates are strategically integrated with PANI, resulting in sensors tailored to different sensing capabilities. Specifically, the proton conduction is heightened by incorporating carboxyl graphene QDs (GQDs) into PANI, obtaining the superior gas-sensing performance for NH3 detection. Moreover, a strain sensor employing Ti3C2Tx QDs (MQDs) and a flexible thermoplastic polyurethane (TPU) substrate has been developed for real-time monitoring of physical biosignals. Finally, a smart sensing network with real-time display integrated with the above sensors is designed to detection NH3 concentration released from pesticide volatilization and physiological tracking in cases of mild pesticide poisoning. This sensing network can wirelessly interface with a laptop for convenient safety monitoring of farmers engaged in smart agriculture. The proof of concept demonstrated by this sensing network holds promise for enhancing safety monitoring in smart agriculture.

Deep learning guided variable rate robotic sprayer prototype

This paper presents the development of a robotic sprayer that combines artificial intelligence with robotics for optimal spray application on citrus nursery plants grown in an indoor environment. The robotic platform is integrated with an embedded firmware of MobileNetV2 model to identify and classify the plant samples with a classification accuracy of 100 % which is used to dispense variable rate spraying of pesticide based on the health status of the plant foliage. The disease detection model was developed through the edge impulse platform and deployed on Raspberry Pi 4. The robot navigates through an array of plants, stops beside each plant, and captures an image of the citrus plants. It feeds the image into the deployed embedded model to generate a disease inference that informs the variable rate application of spray during real-time actuation. To test the spraying performance of the prototype within the growing environment, water sensitive cards were placed in each plant's canopy. After spraying, the samples of water sensitive cards were collected and quantified using a smart spray app to determine the classification accuracy as well as the extent of spray coverage on the citrus samples. The robot spray coverage results show an average spray coverage of 87 % on lemon foliage when compared with 67 % for navel orange, during the spray performance test of the robot.

A learning-based memetic algorithm for a cooperative task allocation problem of multiple unmanned aerial vehicles in smart agriculture

Smart agriculture aligns with the principles of sustainable development, making it a crucial direction for the future agriculture. This study focuses on a cooperative plant protection task allocation problem (CPPTAP) of multiple unmanned aerial vehicles (UAVs) with a common deadline in smart agriculture. CPPTAP permits multiple UAVs to conduct pesticide spraying on the same field. The completion time for each task fluctuates due to the cooperation among UAVs. We present a mathematical model and learning-based memetic algorithm (L-MA) to maximize the total area of the fields to be sprayed. In the evolutionary stage, mutation and repair operators based on value information are applied to balance the exploration and exploitation, while a problem-specific local search strategy is designed to enhance exploitation capability. A knowledge-based UAV allocation method (KUAM) is employed to maximize UAV utilization efficiency and minimize conflicts. Throughout the search process, Q-learning is utilized to assist the aforementioned operators and make decisions on the number of cooperative UAVs on fields. The effectiveness of L-MA is validated by comparing it against other state-of-the-art algorithms. The results demonstrate that L-MA outperforms the compared algorithms at a considerable margin in a statistical sense.