Comparative Study of Major Algorithms for Pest Detection in Maize Crop

Sheema, D.; Ramesh, K.; Renjith, P. N.; Lakshna, A.

doi:10.1109/conit51480.2021.9498280

Cited by 8 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On this basis, Li et al [102] developed a Resnet-50 with the region proposal network (RPN) for pest identification in wheat fields, achieving the accuracies of 90.88%, 88.76%, and 70.2% for wheat sawfly, wheat aphid, and wheat mite, respectively. In addition, the Faster R-CNN model was effectively applied to detect pest infection in grain crops [103]. Furthermore, Verma et al [104] developed three popular CNN models to identify pests in soybean.…”

Section: Crop Insect Infestation Detectionmentioning

confidence: 99%

Convolutional Neural Networks in Computer Vision for Grain Crop Phenotyping: A Review

Wang

2022

Agronomy

View full text Add to dashboard Cite

Computer vision (CV) combined with a deep convolutional neural network (CNN) has emerged as a reliable analytical method to effectively characterize and quantify high-throughput phenotyping of different grain crops, including rice, wheat, corn, and soybean. In addition to the ability to rapidly obtain information on plant organs and abiotic stresses, and the ability to segment crops from weeds, such techniques have been used to detect pests and plant diseases and to identify grain varieties. The development of corresponding imaging systems to assess the phenotypic parameters, yield, and quality of crop plants will increase the confidence of stakeholders in grain crop cultivation, thereby bringing technical and economic benefits to advanced agriculture. Therefore, this paper provides a comprehensive review of CNNs in computer vision for grain crop phenotyping. It is meaningful to provide a review as a roadmap for future research in such a thriving research area. The CNN models (e.g., VGG, YOLO, and Faster R-CNN) used CV tasks including image classification, object detection, semantic segmentation, and instance segmentation, and the main results of recent studies on crop phenotype detection are discussed and summarized. Additionally, the challenges and future trends of the phenotyping techniques in grain crops are presented.

show abstract

Section: Crop Insect Infestation Detectionmentioning

confidence: 99%

Convolutional Neural Networks in Computer Vision for Grain Crop Phenotyping: A Review

Wang

2022

Agronomy

View full text Add to dashboard Cite

show abstract

“…With the progressive increase in global demand for this cereal, crop yield management is a factor that requires great attention to avoid economic losses. Intelligent technologies [ 28 ] are useful for the prior visual recognition of maize insects for decision making and mitigating damage to crops. However, most state-of-the-art machine learning models need high computing power and a large dataset to train algorithms, which are typically deep and complex, making it difficult to use this technology for on-site applications in agriculture.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Layer Feature Fusion Method for Few-Shot Image Classification

Gomes

Borges

2023

Sensors

View full text Add to dashboard Cite

In image classification, few-shot learning deals with recognizing visual categories from a few tagged examples. The degree of expressiveness of the encoded features in this scenario is a crucial question that needs to be addressed in the models being trained. Recent approaches have achieved encouraging results in improving few-shot models in deep learning, but designing a competitive and simple architecture is challenging, especially considering its requirement in many practical applications. This work proposes an improved few-shot model based on a multi-layer feature fusion (FMLF) method. The presented approach includes extended feature extraction and fusion mechanisms in the Convolutional Neural Network (CNN) backbone, as well as an effective metric to compute the divergences in the end. In order to evaluate the proposed method, a challenging visual classification problem, maize crop insect classification with specific pests and beneficial categories, is addressed, serving both as a test of our model and as a means to propose a novel dataset. Experiments were carried out to compare the results with ResNet50, VGG16, and MobileNetv2, used as feature extraction backbones, and the FMLF method demonstrated higher accuracy with fewer parameters. The proposed FMLF method improved accuracy scores by up to 3.62% in one-shot and 2.82% in five-shot classification tasks compared to a traditional backbone, which uses only global image features.

show abstract