An Internet of Things assisted Unmanned Aerial Vehicle based artificial intelligence model for rice pest detection

This paper explores the utilization of innovative technologies such as RGB cameras, drones, and computer vision algorithms, for monitoring pests in orchards, with a specific focus on detecting the Halyomorpha halys (HH), commonly known as the “brown marmorated stink bug”. The integration of drones and machine learning (ML) into integrated pest management shows promising potential for effectively combating HH infestations. However, challenges arise from relying on vision models solely trained using high-quality images from public datasets. To address this issue, we create an ad hoc dataset of on-site images mainly captured with the help of a drone as well as other devices. We initially conduct an in-depth analysis of the captured images, considering factors such as blurriness and brightness, to possibly improve the performance of the ML algorithms. Afterwards, we undertake the training and evaluation of diverse ML models using distinct approaches within the YOLO framework. We employ a range of metrics to compare their performance and ultimately achieve a satisfactory outcome. Through the optimization of ML models and the correction of image imperfections, we contribute to advancing automated decision-making processes in pest insect monitoring and management, specifically in HH monitoring. 

YOLO-based detection of Halyomorpha halys in orchards using RGB cameras and drones

Maintenance of Crop health is essential for the successful farming for both yield and product quality. Pest and disease in crops are serious problem to be monitored. pest and disease occur in different stages or phases of crop development. Due to introduction of genetically modified seeds the natural resistance of crops to prevent them from pest and disease is less. Major crop loss is due to pest and disease attack in crops. It damages the leaves, buds, flowers and fruits of the crops. Affected areas and damage levels of pest and diseases attacks are growing rapidly based on global climate change. Weather Conditions plays a major role in pest and disease attacks in crops. Naked eye inspection of pest and disease is complex and difficult for wide range of field. And at the same time taking lab samples to detect disease is also inefficient and time-consuming process. Early identification of diseases is important to take necessary actions for preventing crop loss and to avoid disease spreads. So, Timely and effective monitoring of crop health is important. Several technologies have been developed to detect pest and disease in crops. In this paper we discuss the various technologies implemented by using AI and Deep Learning for pest and disease detection. And also, briefly discusses their Advantages and limitations on using certain technology for monitoring of crops.

A Survey on Pest and Disease Monitoring of Crops

Insect pests have always been one of the main hazards affecting crop yield and quality in traditional agriculture. An accurate and timely pest detection algorithm is essential for effective pest control; however, the existing approach suffers from a sharp performance drop when it comes to the pest detection task due to the lack of learning samples and models for small pest detection. In this paper, we explore and study the improvement methods of convolutional neural network (CNN) models on the Teddy Cup pest dataset and further propose a lightweight and effective agricultural pest detection method for small target pests, named Yolo-Pest, for the pest detection task in agriculture. Specifically, we tackle the problem of feature extraction in small sample learning with the proposed CAC3 module, which is built in a stacking residual structure based on the standard BottleNeck module. By applying a ConvNext module based on the vision transformer (ViT), the proposed method achieves effective feature extraction while keeping a lightweight network. Comparative experiments prove the effectiveness of our approach. Our proposal achieves 91.9% mAP0.5 on the Teddy Cup pest dataset, which outperforms the Yolov5s model by nearly 8% in mAP0.5. It also achieves great performance on public datasets, such as IP102, with a great reduction in the number of parameters.

/pdf/yolo-pest-an-insect-pest-object-detection-algorithm-via-cac3-30v850yc.pdf

Yolo-Pest: An Insect Pest Object Detection Algorithm via CAC3 Module

Litchi leaf diseases and pests can lead to issues such as a decreased Litchi yield, reduced fruit quality, and decreased farmer income. In this study, we aimed to explore a real-time and accurate method for identifying Litchi leaf diseases and pests. We selected three different orchards for field investigation and identified five common Litchi leaf diseases and pests (Litchi leaf mite, Litchi sooty mold, Litchi anthracnose, Mayetiola sp., and Litchi algal spot) as our research objects. Finally, we proposed an improved fully convolutional one-stage object detection (FCOS) network for Litchi leaf disease and pest detection, called FCOS for Litch (FCOS-FL). The proposed method employs G-GhostNet-3.2 as the backbone network to achieve a model that is lightweight. The central moment pooling attention (CMPA) mechanism is introduced to enhance the features of Litchi leaf diseases and pests. In addition, the center sampling and center loss of the model are improved by utilizing the width and height information of the real target, which effectively improves the model’s generalization performance. We propose an improved localization loss function to enhance the localization accuracy of the model in object detection. According to the characteristics of Litchi small target diseases and pests, the network structure was redesigned to improve the detection effect of small targets. FCOS-FL has a detection accuracy of 91.3% (intersection over union (IoU) = 0.5) in the images of five types of Litchi leaf diseases and pests, a detection rate of 62.0/ms, and a model parameter size of 17.65 M. Among them, the detection accuracy of Mayetiola sp. and Litchi algal spot, which are difficult to detect, reached 93.2% and 92%, respectively. The FCOS-FL model can rapidly and accurately detect five common diseases and pests in Litchi leaf. The research outcome is suitable for deployment on embedded devices with limited resources such as mobile terminals, and can contribute to achieving real-time and precise identification of Litchi leaf diseases and pests, providing technical support for Litchi leaf diseases’ and pests’ prevention and control.

/pdf/detection-of-litchi-leaf-diseases-and-insect-pests-based-on-2ndyxr3k.pdf

Detection of Litchi Leaf Diseases and Insect Pests Based on Improved FCOS

Agriculture is an essential source of sustenance In India, this sector has tremendous opportunities of large-scale employment for villagers A survey report illustrated the dependency of the Indian population on agriculture ie nearly 70% Here, agriculture consist of the composition of several crops depending on the climatic nature However, most of the Indian farmers are still unaware of technical knowledge such as what kind of crop suits their farmland Numerous heterogeneous diseases affect the production of crops and result as a profitable loss This paper illustrates the pest diseases specifically with their impact on the current production of the crop In addition, it shows the survey reports based on several detection techniques of image detection It is important to search and develop more techniques in order to identify the pest disease before it creates a serious loss in crop production The current method for the reduction of pest disease is to spray pesticides However, this process severely affects the health of humans directly or indirectly The pest detection techniques at the early stages can provide less need for spraying pesticides The image detection technique emerges as an effective measurement tool in order to fight the infestation This technique offers better crop management with production as it delivers the maximum protection to crops Such techniques also minimize human errors and efforts as providing the feature of automatic monitoring over large fields

A Comprehensive Survey on Pest Detection Techniques using Image Processing

A survey report showed that 70% Indian population depends on agriculture sector. Numerous heterogeneous diseases and various kind of pests affect the production of crops which leads to quality and quantitative loss. Automatic in-field pest detection using computer vision technique is an important topic in modern intelligent agriculture but suffers from serious challenges including complexity of wild environment, detection of tiny size pest and classification into multiple classes of pests. In the literature attention is mostly focused on machine learning based techniques and image processing has not received equal attention. This paper illustrates an automatic approach for pest detection using Wavelet transformation and Oriented FAST and rotated BRIEF (ORB). The core objective of the research is to enhance feature extraction phase to improve the detection efficiency. The proposed approach is implemented on images of fluffy caterpillar pests on mustard crop and fava bean collected from farms in Rajasthan. The experimental results affirm the efficiency of the proposed approach.

Pest Detection on Leaf using Image Processing

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A Comprehensive Survey on Pest Detection Techniques using Image Processing

Pest Detection on Leaf using Image Processing