Pest Detection Research Articles

Detection of the Pine Wilt Disease Using a Joint Deep Object Detection Model Based on Drone Remote Sensing Data

Disease and detection is crucial for the protection of forest growth, reproduction, and biodiversity. Traditional detection methods face challenges such as limited coverage, excessive time and resource consumption, and poor accuracy, diminishing the effectiveness of forest disease prevention and control. By addressing these challenges, this study leverages drone remote sensing data combined with deep object detection models, specifically employing the YOLO-v3 algorithm based on loss function optimization, for the efficient and accurate detection of tree diseases and pests. Utilizing drone-mounted cameras, the study captures insect pest image information in pine forest areas, followed by segmentation, merging, and feature extraction processing. The computing system of airborne embedded devices is designed to ensure detection efficiency and accuracy. The improved YOLO-v3 algorithm combined with the CIoU loss function was used to detect forest pests and diseases. Compared to the traditional IoU loss function, CIoU takes into account the overlap area, the distance between the center of the predicted frame and the actual frame, and the consistency of the aspect ratio. The experimental results demonstrate the proposed model’s capability to process pest and disease images at a slightly faster speed, with an average processing time of less than 0.5 s per image, while achieving an accuracy surpassing 95%. The model’s effectiveness in identifying tree pests and diseases with high accuracy and comprehensiveness offers significant potential for developing forest inspection protection and prevention plans. However, limitations exist in the model’s performance in complex forest environments, necessitating further research to improve model universality and adaptability across diverse forest regions. Future directions include exploring advanced deep object detection models to minimize computing resource demands and enhance practical application support for forest protection and pest control.

Improved YOLOv8 and SAHI Model for the Collaborative Detection of Small Targets at the Micro Scale: A Case Study of Pest Detection in Tea

Pest target identification in agricultural production environments is challenging due to the dense distribution, small size, and high density of pests. Additionally, changeable environmental lighting and complex backgrounds further complicate the detection process. This study focuses on enhancing the recognition performance of tea pests by introducing a lightweight pest image recognition model based on the improved YOLOv8 architecture. First, slicing-aided fine-tuning and slicing-aided hyper inference (SAHI) are proposed to partition input images for enhanced model performance on low-resolution images and small-target detection. Then, based on an ELAN, a generalized efficient layer aggregation network (GELAN) is designed to replace the C2f module in the backbone network, enhance its feature extraction ability, and construct a lightweight model. Additionally, the MS structure is integrated into the neck network of YOLOv8 for feature fusion, enhancing the extraction of fine-grained and coarse-grained semantic information. Furthermore, the BiFormer attention mechanism, based on the Transformer architecture, is introduced to amplify target characteristics of tea pests. Finally, the inner-MPDIoU, based on auxiliary borders, is utilized as a replacement for the original loss function to enhance its learning capacity for complex pest samples. Our experimental results demonstrate that the enhanced YOLOv8 model achieves a precision of 96.32% and a recall of 97.95%, surpassing those of the original YOLOv8 model. Moreover, it attains an mAP@50 score of 98.17%. Compared to Faster R-CNN, SSD, YOLOv5, YOLOv7, and YOLOv8, its average accuracy is 17.04, 11.23, 5.78, 3.75, and 2.71 percentage points higher, respectively. The overall performance of YOLOv8 outperforms that of current mainstream detection models, with a detection speed of 95 FPS. This model effectively balances lightweight design with high accuracy and speed in detecting small targets such as tea pests. It can serve as a valuable reference for the identification and classification of various insect pests in tea gardens within complex production environments, effectively addressing practical application needs and offering guidance for the future monitoring and scientific control of tea insect pests.

UAV Remote Sensing Technology: A Review of Chinese Agricultural Patents

Background: With the progress of science and technology and the advent of the new era, UAV remote sensing technology has developed rapidly in recent years and the application fields have been increasing. UAV remote sensing technology is widely used in the field of agriculture, crop growth monitoring, crop yield prediction, crop information monitoring, pest and disease detection and other aspects are more prominent. Objective: By analyzing and discussing the existing Chinese patents on UAV remote sensing technology, we summarize the current applications and future development prospects of UAV remote sensing technology in China's agricultural field. Methods: An overview of current Chinese patents on different directions of UAV remote sensing technology in the agricultural field, and an analysis of the methods used to achieve different application implementation effects. Results: By analyzing the existing Chinese patents on UAV remote sensing technology, we conclude that the functions they can achieve for different crops can be applied to the data collection of all crops, and analyze their characteristics to summarize future development trends. Conclusion: Through an in-depth study of Chinese UAV remote sensing patents in agriculture, it can be concluded that UAV remote sensing technology can achieve higher applicability and lower cost in agriculture, and for its information collection for one crop, it can be extended to apply to all crops. In the future, UAV remote sensing technology can be more easily and quickly applied to crop information acquisition in the agricultural field.

Detection of Insects and Pests in Agriculture field using MobileNet

Abstract: The Indian economy heavily relies on agriculture, with high-quality crop production playing a pivotal role. However, frequent pest attacks pose significant threats by reducing crop yields and compromising food safety through nutrient depletion. This adversely impacts the economy, leading to substantial losses for farmers and risking lives. Timely monitoring of crops is imperative to combat pests effectively, necessitating the use of appropriate pesticides. Pest detection technologies can aid in early intervention, preventing crop damage and pesticide overuse. Artificial intelligence (AI) emerges as a crucial tool in addressing agricultural challenges. This research focuses on utilizing the MobileNetV2 algorithm for pest classification, leveraging image reshaping and feature extraction techniques. Results indicate MobileNetV2 outperforms other pre-trained models, achieving a higher accuracy of 0.95. By enhancing pest detection capabilities, AI-based technologies offer promising solutions to bolster agricultural production and mitigate economic losses.

E-Citrus: A Cloud-Based Citrus Pest and Disease Detection, Diagnostic and Prevention using Convolutional Neural Network

Citrus fruit yields in the Philippines have been fluctuating dramatically in recent years. Diseases, pests, and soil inadequacy have all contributed to the citrus industry's severe decline. More than 15 viruses and virus-like diseases have infected Citrus. Agricultural productivity must improve for a country to be progressive. Resources should be utilized to their full potential, diseases and pests should be controlled efficiently, and technological advancements must be adopted. This application will identify and map common pests and diseases of citrus fruits in Oriental Mindoro, apply image processing techniques to analyze diseases of citrus fruits with corresponding solutions caused by bacteria, and give information about diseases related to citrus fruits and how to cure them. The researchers used the Spiral Model as a Software Development Life Cycle (SDLC) model to develop this application. In this model, researchers can plan the flow of the application. If the application did not meet the desired result, the researchers could revise it again until it met the desired one. The researchers used the convolutional neural network to classify and process the captured images of the citrus fruits’ diseases and pests. The researchers asked the selected Citrus farmers in Oriental Mindoro to evaluate the project using the different ISO 25010 criteria and rated the application as very acceptable overall.

Leveraging Deep Learning and Farmland Fertility Algorithm for Automated Rice Pest Detection and Classification Model

Leveraging Deep Learning and Farmland Fertility Algorithm for Automated Rice Pest Detection and Classification Model

Ragi Pest Control

The rise of global population has put increasing pressure on the agriculture industry to meet the demand for food. However, the growing use of pesticides and insecticides in conventional farming practices has caused significant harm to the environment and human health. Thus, there is a growing interest in using sustainable agriculture practices that reduce the use of these harmful chemicals. One such practice is pest detection, which enables farmers to detect pests in their crops before they cause significant damage. In this context, this project aims to develop a pest detection system using IoT and Arduino. The system will be designed to detect pests in crops through a combination of sensors and machine learning algorithms. The system will consist of an Arduino microcontroller, soil moisture sensors, temperature and humidity sensors, infrared sensors or camera modules, and a Wi-Fi or Bluetooth module. The sensors will collect data on soil moisture levels, temperature, humidity, and pest activity. The data will be sent to a cloud-based server or database for analysis and visualization. The infrared sensor or camera module will detect the presence of pests in the crops. The system will use machine learning algorithms to distinguish between pests and other objects, such as leaves or debris. When pests are detected, the system will alert the farmer through a buzzer or LED connected to the Arduino board. The farmer can then take appropriate action, such as applying pesticide or removing infested plants. The pest detection system has the potential to reduce the use of harmful pesticides and insecticides in agriculture, as farmers will be able to identify pests before they cause significant damage. The system will also provide farmers with real-time information on pest activity, enabling them to take proactive measures to control pests and reduce crop damage. Additionally, the system can track and store the pest detection data over time, allowing farmers to monitor trends and patterns in pest activity. In conclusion, this project proposes the development of a pest detection system using IoT and Arduino that will enable farmers to monitor pest activity in their crops in real-time. The system has the potential to reduce the use of harmful chemicals in agriculture and improve crop yield while ensuring sustainable and environmentally friendly practices.

IoT Based Smart Irrigation System using Artificial Intelligence

This proposed of the project introduces an innovative IoT-based AI-driven solution engineered to revolutionize water management in agricultural contexts, catering to both large-scale piped irrigation networks and micro-irrigation systems. Our proposed system leverages cutting-edge AI algorithms to forecast dynamic crop water requirements, seamlessly integrating real-time soil moisture data to optimize water utilization and amplify crop yields. By amalgamating AI-driven predictive analytics with instantaneous sensor feedback, our system facilitates proactive water resource management, curbing wastage and mitigating environmental repercussions. Moreover, the integration of Deep Learning-based pest detection augments the system's capabilities by safeguarding crops against potential threats, thus fostering sustainable agricultural practices. Additionally, our solution pioneers the incorporation of Deep Learning-based crop production for the detection of wild animals, thereby enabling timely alerts through buzzer sound notifications, thereby ensuring comprehensive crop protection. This amalgamation of IoT, AI, and Deep Learning technologies promises to significantly enhance agricultural sustainability, optimize resource utilization, and mitigate ecological impact, thus paving the way for a more resilient and productive agricultural ecosystem.

Ai Driven Interactive Agri Bot Providing Realtime Assistance in Cultivation and Market Linkages

This project introduces an integrated system for smart agriculture, employing Internet of Things (IoT) technology for soil type analysis and deep learning methodologies for pest detection. The proposed system leverages a specialized NPK sensor for real-time measurement of soil nutrient levels, facilitating precision agriculture practices. Additionally, a Convolutional Neural Network (CNN) algorithm is employed to detect pests in crops, enhancing crop management efficiency and yield optimization. The IoT-based NPK sensor enables farmers to monitor essential soil nutrients such as nitrogen (N), phosphorus (P), and potassium (K) levels remotely and in real-time. This data empowers farmers to make informed decisions regarding fertilization strategies, ensuring optimal nutrient balance for healthy plant growth while minimizing resource wastage and environmental impact. To the deep learning framework, specifically CNN, is utilized for pest detection in crops. By analyzing images captured from smart agricultural cameras, the CNN model can identify and classify various pests and diseases affecting crops. This enables early detection and intervention, thereby mitigating potential crop damage and yield losses. The integration of CNN-based pest detection with IoT infrastructure enables timely and targeted pest management actions, reducing reliance on chemical pesticides and promoting sustainable agricultural practices.

EResNet-SVM: an overfitting-relieved deep learning model for recognition of plant diseases and pests.

The accurate recognition and early warning for plant diseases and pests are a prerequisite of intelligent prevention and control for plant diseases and pests. As a result of the phenotype similarity of the hazarded plant after plant diseases and pests occur, as well as the interference of the external environment, traditional deep learning models often face the overfitting problem in phenotype recognition of plant diseases and pests, which leads to not only the slow convergence speed of the network, but also low recognition accuracy. Motivated by the above problems, the present study proposes a deep learning model EResNet-support vector machine (SVM) to alleviate the overfitting for the recognition and classification of plant diseases and pests. First, the feature extraction capability of the model is improved by increasing feature extraction layers in the convolutional neural network. Second, the order-reduced modules are embedded and a sparsely activated function is introduced to reduce model complexity and alleviate overfitting. Finally, a classifier fused by SVM and fully connected layers are introduced to transforms the original non-linear classification problem into a linear classification problem in high-dimensional space to further alleviate the overfitting and improve the recognition accuracy of plant diseases and pests. The ablation experiments further demonstrate that the fused structure can effectively alleviate the overfitting and improve the recognition accuracy. The experimental recognition results for typical plant diseases and pests show that the proposed EResNet-SVM model has 99.30% test accuracy for eight conditions (seven plant diseases and one normal), which is 5.90% higher than the original ResNet18. Compared with the classic AlexNet, GoogLeNet, Xception, SqueezeNet and DenseNet201 models, the accuracy of the EResNet-SVM model has improved by 5.10%, 7%, 8.10%, 6.20% and 1.90%, respectively. The testing accuracy of the EResNet-SVM model for 6 insect pests is 100%, which is 3.90% higher than that of the original ResNet18 model. This research provides not only useful references for alleviating the overfitting problem in deep learning, but also a theoretical and technical support for the intelligent detection and control of plant diseases and pests. © 2024 Society of Chemical Industry.

An effective segmentation and attention-based reptile residual capsule auto encoder for pest classification.

Insect pests are a major global factor affecting agricultural crop productivity and quality. Rapid and precise insect pest detection is crucial for improving handling and prediction techniques. There are several methods for pest detection and classification tasks; still, the inaccurate detection, computation complexity and several other challenges affect the performance of the model. Thus, this research presents a Deep Learning (DL) approach that has led to significant advancements and is currently being applied successfully in many domains, such as autonomous insect pest detection. Initially, the input images are gathered from the test dataset. The next step in pre-processing the input images is to improve the model capacity by removing unwanted data using the Enhanced Kuan filter method. Then, the pre-processed images are segmented using the Attention-based U-Net method. Finally, a novel Attention Based Reptile Residual Capsule Auto Encoder (ARRCAE) technique is proposed to classify and recognize crop pests. Furthermore, the Improved Reptile Search Optimisation (IRSO) algorithm is employed to fine-tune the classification parameters optimally. As a result, the proposed study enhances performance by classifying crop pest detection systems. The suggested method makes use of a Python tool for simulation, and pest datasets are utilized for result analysis. The suggested model beats other current models with an accuracy of 98%, precision of 97%, recall of 96%, and specificity of 99% for the pest dataset, per the simulation results that were obtained. © 2024 Society of Chemical Industry.

Research Progress in Detection and Identification of Crop Diseases and Insect Pests Based on Deep Learning

Research Progress in Detection and Identification of Crop Diseases and Insect Pests Based on Deep Learning

Detection of pine wood nematode infestation using hyperspectral drone images

The pine wood nematode (PWN) is one of the most threatening pests of pine forests in China. An essential management response involves felling and removing the infested trees before the PWN can spread over large areas, but this requires early detection. In this study, we analyzed the temporal changes in the spectral signatures of healthy and infested trees throughout the entire growing season (May to October) using unmanned aerial vehicle (UAV) hyperspectral images (HSI). To explore the potential of these seasonal spectral changes in detecting PWN, we investigated the separability of healthy and infested trees using both absolute values and changes in values. A random forest (RF) classifier was applied to separate healthy and infested trees based on reflectances, derivatives, vegetation indices (VIs), and their seasonal change values. We obtained the following results: (1) the seasonal changes of tree crown spectra following a PWN infestation were similar to those observed using absolute spectral values, though the latter performed better in early detection; (2) in the early stage of infestation (June and July), the green and red-edge regions contained the most sensitive bands, whereas the near-infrared portion played a more important role in later stages; (3) derivatives achieved higher overall accuracy (OA) than the reflectance in the early stage of PWN infestation (June and July); (4) in June, the Pigment Specific Simple Ratio (PSSR) had the highest separability (OA = 0.77) for PWN infestation, followed by the Plant Senescence Reflectance Index (PSRI) and the Normalized Difference Red-edge Index (NDRE), both with a OA of 0.70. In July, PSRI, Red Edge Normalized Difference Vegetation Index (RENDVI), and the Modified Red Edge Simple Ratio (MRESR) had the highest OA of 0.77. These VIs were calculated based on the red-edge regions. In August, RENDVI achieved the best accuracy (0.87), followed by the Normalized Difference Vegetation Index (NDVI, OA = 0.83) and the Plant Stress Index (PSI, OA = 0.80). In September and October, PSRI, PSI, the Greenness Index (GI), and RENDVI showed the strongest separability (0.87 to 0.90). Similar results were obtained using change values of VIs. In June, RENDVI, PSI, and NDVI showed the highest accuracy (OA = 0.73). In July, MRESR had the best accuracy (0.77), followed by RENDVI (0.73) and PSI (0.73). In the late stage of PWN infestation, NDVI, PSRI, PSI, RENDVI, GI, and PSSR obtained the highest accuracies (0.83 to 0.93), surpassing the accuracy achieved using absolute spectra; (5) in RF classification model, seasonal variation values derived from reflectances, derivatives, and VIs performed well in the late stages of the PWN infestation (September and October) but were not as good as absolute values in the early and middle stages (June to August). Our study explored the potential of seasonal changes in UAV spectra for early detection of forest pests.

Internet of Things assisted Unmanned Aerial Vehicle for Pest Detection with Optimized Deep Learning Model

IoT technologies & UAVs are frequently utilized in ecological monitoring areas. Unmanned Aerial Vehicles (UAVs) & IoT in farming technology can evaluate crop disease & pest incidence from the ground’s micro & macro aspects, correspondingly. UAVs could capture images of farms using a spectral camera system, and these images are been used to examine the presence of agricultural pests and diseases. In this research work, a novel IoT- assisted UAV- based pest detection with Arithmetic Crossover based Black Widow Optimization-Convolutional Neural Network (ACBWO-CNN) model is developed in the field of agriculture. Cloud computing mechanism is used for monitoring and discovering the pest during crop production by using UAVs. The need for this method is to provide data centers, so there is a necessary amount of memory storage in addition to the processing of several images. Initially, the collected input image by the UAV is assumed on handling the via-IoT-cloud server, from which the pest identification takes place. The pest detection unit will be designed with three major phases: (a) background &foreground Segmentation, (b) Feature Extraction & (c) Classification. In the foreground and background Segmentation phase, the K-means clustering will be utilized for segmenting the pest images. From the segmented images, it extracts the features including Local Binary Pattern (LBP) &improved Local Vector Pattern (LVP) features. With these features, the optimized CNN classifier in the classification phase will be trained for the identification of pests in crops. Since the final detection outcome is from the Convolutional Neural Network (CNN); its weights are fine-tuned through the ACBWO approach. Thus, the output from optimized CNN will portray the type of pest identified in the field. This method’s performance is compared to other existing methods concerning a few measures.

Intelligent technologies and their transformative role in modern agriculture: A comparative approach

The escalating global demand for food, propelled by a burgeoning population and the unpredictable shifts in climatic conditions, presents a challenge that traditional plant breeding alone struggles to address. In response to this pressing need, the infusion of intelligent technologies emerges as a pivotal solution, poised not only to boost production but also to meet the burgeoning demand. This transformative approach encompasses a spectrum of cutting-edge tools, including Remote Sensing and GIS, Aeroponics, Drone Technology, Biotechnology, Artificial Intelligence, Machine Learning, and, ultimately, Robotics. The synergistic integration of these technologies will enhance agricultural monitoring by facilitating precise crop surveillance, early detection and mitigation of diseases and pests, optimization of water resources, accurate mapping of land use and crop types, comprehensive environmental monitoring, real-time weather and climate tracking, efficient nutrient management, precise irrigation and spraying practices, reliable yield prediction, advanced demand forecasting, genetic analysis, and informed decision-making processes. The amalgamation of intelligent technologies with modern plant breeding methodologies signifies a significant advancement towards achieving more efficient and sustainable agricultural practices. This convergence not only addresses the immediate need for increased food production but also sets the stage for a resilient and future-ready agricultural landscape. In this era of integration, we witness the harmonious coexistence of tradition and innovation, paving the way for a more abundant and secure agricultural future.

Integrating 5G and machine learning technologies for advanced PDM in smart farming

Smart farming is revolutionizing agriculture by integrating advanced technologies to enhance productivity, efficiency, and sustainability. This paper proposes a novel, 5G-enabled Pest and Disease Detection and Response System (PDDRS) that synergizes environmental sensor data with image analytics for comprehensive Plant Disease Detection (PDD). By leveraging the high bandwidth and ultra-low latency capabilities of 5G, our integrated system surpasses traditional communication technologies, facilitating real-time data analytics and immediate intervention strategies. We introduce two Machine Learning (ML) models: an image-based Mask R-CNN with FPN, which achieves a precision of 91.1% and an accuracy of 95.1%, and an environmental-based FFNN + LSTM model, evaluated for ACC, AUC, and F1-Score, showing promising results in disease forecasting. Our experiments demonstrate that the PDDRS significantly enhances throughput and latency performance under various connected devices, showcasing a scalable, cost-effective solution suitable for next-generation smart farming. These advancements collectively empower the PDDRS to deliver actionable insights, enabling targeted applications such as precise pesticide deployment, and stand as a testament to the potential of 5G in agricultural innovation.

RICE-YOLO: In-Field Rice Spike Detection Based on Improved YOLOv5 and Drone Images

The rice spike, a crucial part of rice plants, plays a vital role in yield estimation, pest detection, and growth stage management in rice cultivation. When using drones to capture photos of rice fields, the high shooting angle and wide coverage area can cause rice spikes to appear small in the captured images and can cause angular distortion of objects at the edges of images, resulting in significant occlusions and dense arrangements of rice spikes. These factors are unique challenges during drone image acquisition that may affect the accuracy of rice spike detection. This study proposes a rice spike detection method that combines deep learning algorithms with drone perspectives. Initially, based on an enhanced version of YOLOv5, the EMA (efficient multiscale attention) attention mechanism is introduced, a novel neck network structure is designed, and SIoU (SCYLLA intersection over union) is integrated. Experimental results demonstrate that RICE-YOLO achieves a mAP@0.5 of 94.8% and a recall of 87.6% on the rice spike dataset. During different growth stages, it attains an AP@0.5 of 96.1% and a recall rate of 93.1% during the heading stage, and a AP@0.5 of 86.2% with a recall rate of 82.6% during the filling stage. Overall, the results indicate that the proposed method enables real-time, efficient, and accurate detection and counting of rice spikes in field environments, offering a theoretical foundation and technical support for real-time and efficient spike detection in the management of rice growth processes.

Efficient Model on Corp Disease and Pest Detection with Deep Learning

Agricultural production faces significant challenges due to pests and diseases, causing substantial losses in crop yield globally. Traditional methods of pest and disease management are often manual, time-consuming, and prone to errors. In recent years, the integration of artificial intelligence (AI) techniques, particularly deep learning algorithms, with modern information and communication technology has shown promising results in addressing these challenges. This paper presents a comprehensive review of recent advancements in applying deep learning for detecting and classifying agricultural pests, diseases, and weeds. Various deep learning models, including convolutional neural networks (CNNs) such as Faster R-CNN, InceptionV3, DenseNet, and AlexNet, have been explored for their efficacy in identifying pests, diseases, and weeds in crops. While models like Faster R-CNN, InceptionV3, and DenseNet have demonstrated high accuracy rates ranging from 78.71% to 99.62% in classification tasks across different datasets and crops, the AlexNet architecture has also shown promising results in certain applications within agricultural image analysis. Additionally, the development of lightweight CNN architectures and the fusion of deep features with traditional handcrafted features have further enhanced the accuracy and efficiency of detection systems. Furthermore, the review discusses challenges and future research directions in the field, emphasizing the importance of large-scale datasets, model optimization, and real-time applications for practical implementation in agriculture. Overall, the findings highlight the potential of deep learning technologies, including models like AlexNet, in revolutionizing pest and disease management practices, leading to improved crop yield, food security, and sustainable agriculture. Keywords: Agricultural pests, plant diseases, deep learning, convolutional neural networks (CNNs), AlexNet, classification

A Time-Frequency Domain Mixed Attention-Based Approach for Classifying Wood-Boring Insect Feeding Vibration Signals Using a Deep Learning Model.

Wood borers, such as the emerald ash borer and holcocerus insularis staudinger, pose a significant threat to forest ecosystems, causing damage to trees and impacting biodiversity. This paper proposes a neural network for detecting and classifying wood borers based on their feeding vibration signals. We utilize piezoelectric ceramic sensors to collect drilling vibration signals and introduce a novel convolutional neural network (CNN) architecture named Residual Mixed Domain Attention Module Network (RMAMNet).The RMAMNet employs both channel-domain attention and time-domain attention mechanisms to enhance the network's capability to learn meaningful features. The proposed system outperforms established networks, such as ResNet and VGG, achieving a recognition accuracy of 95.34% and an F1 score of 0.95. Our findings demonstrate that RMAMNet significantly improves the accuracy of wood borer classification, indicating its potential for effective pest monitoring and classification tasks. This study provides a new perspective and technical support for the automatic detection, classification, and early warning of wood-boring pests in forestry.

Segmentation and detection of crop pests using novel U-Net with hybrid deep learning mechanism.

In India, agriculture is the backbone of economic sectors because of the increasing demand for agricultural products. However, agricultural production has been affected due to the presence of pests in crops. Several methods were developed to solve the crop pest detection issue, but they failed to achieve better results. Therefore, the proposed study used a new hybrid deep learning mechanism for segmenting and detecting pests in crops. Image collection, pre-processing, segmentation, and detection are the steps involved in the proposed study. There are three steps involved in pre-processing: image rescaling, equalized joint histogram based contrast enhancement (Eq-JH-CE), and bendlet transform based De-noising (BT-D). Next, the pre-processed images are segmented using the DenseNet-77 UNet model. In this section, the complexity of the conventional UNet model is mitigated by hybridizing it with the DenseNet-77 model. Once the segmentation is done with an improved model, the crop pests are detected and classified by proposing a novel Convolutional Slice-Attention based Gated Recurrent Unit (CS-AGRU) model. The proposed model is the combination of a convolutional Neural Network (CNN) and a Gated Recurrent Unit (GRU). In order to achieve better accuracy outcomes, the proposed study hybridized these models due to their great efficiency. Also, the slice attention mechanism is applied over the proposed model for fetching relevant feature information and thereby enhancing the computational efficiency. So, pests in the crop are finally detected using the proposed method. The Python programming language is utilized for implementation. The proposed approach shows a better accuracy range of 99.52%, IoU of 99.1%, precision of 98.88%, recall of 99.53%, F1-score of 99.35%, and FNR of 0.011 compared to existing techniques. Identifying and classifying pests helps farmers anticipate potential threats to their crops. By knowing which pests are prevalent in their region or are likely to infest certain crops, farmers can implement preventive measures to protect their crops, such as planting pest-resistant varieties, using crop rotation, or deploying traps and barriers. © 2024 Society of Chemical Industry.