Application of agricultural insect pest detection and control map based on image processing analysis

Deng, Leilei; Wang, Zhenghao; Wang, Chuang; He, Yifan; Huang, Tao; Yue, Dong; Zhou, Xian

doi:10.3233/jifs-179413

Cited by 17 publications

(9 citation statements)

References 11 publications

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“…In contrast, the Intel RealSense D435i camera employed in this research possesses the capability to concurrently capture color imagery, depth maps, and 3D point clouds of maize stems. Color data play a pivotal role in aiding researchers to diagnose crop diseases and infestations ( Deng et al., 2020 ). Additionally, this camera is not only economical and portable but also amenable to further development.…”

Section: Discussionmentioning

confidence: 99%

Detection of maize stem diameter by using RGB-D cameras’ depth information under selected field condition

Zhou,

Cui,

et al. 2024

Front. Plant Sci.

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Stem diameter is a critical phenotypic parameter for maize, integral to yield prediction and lodging resistance assessment. Traditionally, the quantification of this parameter through manual measurement has been the norm, notwithstanding its tedious and laborious nature. To address these challenges, this study introduces a non-invasive field-based system utilizing depth information from RGB-D cameras to measure maize stem diameter. This technology offers a practical solution for conducting rapid and non-destructive phenotyping. Firstly, RGB images, depth images, and 3D point clouds of maize stems were captured using an RGB-D camera, and precise alignment between the RGB and depth images was achieved. Subsequently, the contours of maize stems were delineated using 2D image processing techniques, followed by the extraction of the stem’s skeletal structure employing a thinning-based skeletonization algorithm. Furthermore, within the areas of interest on the maize stems, horizontal lines were constructed using points on the skeletal structure, resulting in 2D pixel coordinates at the intersections of these horizontal lines with the maize stem contours. Subsequently, a back-projection transformation from 2D pixel coordinates to 3D world coordinates was achieved by combining the depth data with the camera’s intrinsic parameters. The 3D world coordinates were then precisely mapped onto the 3D point cloud using rigid transformation techniques. Finally, the maize stem diameter was sensed and determined by calculating the Euclidean distance between pairs of 3D world coordinate points. The method demonstrated a Mean Absolute Percentage Error (MAPE) of 3.01%, a Mean Absolute Error (MAE) of 0.75 mm, a Root Mean Square Error (RMSE) of 1.07 mm, and a coefficient of determination (R²) of 0.96, ensuring accurate measurement of maize stem diameter. This research not only provides a new method of precise and efficient crop phenotypic analysis but also offers theoretical knowledge for the advancement of precision agriculture.

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Section: Discussionmentioning

confidence: 99%

Detection of maize stem diameter by using RGB-D cameras’ depth information under selected field condition

Zhou,

Cui,

et al. 2024

Front. Plant Sci.

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“…It exhibited a sensitivity of up to 98.8 percent for recognising and diagnosing rice leaf diseases. [11] In agricultural disease and pest image processing and classification approaches, PCA and SVM are employed extensively. [12] The objective function of these approaches depends primarily on the Euclidean distance measure, which requires that the input sample space is isotropic.…”

Section: Literature Surveymentioning

confidence: 99%

The Novel ML Approaches on Wheat Disease by CLF and FOHF of Image Equalization Techniques

Kanna.R¹,

V.²

2023

INDJCSE

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Crop diseases and insect pests are the most significant obstacles to agricultural production. Consequences include a decrease in crop yields and the obstruction of the long-term development of high-quality, high-efficiency agriculture. Rapid and accurate detection of wheat leaf disease categories promotes early deployment of field control and improvement of wheat yield and quality. It's no exaggeration to say that wheat is one of the world's most vital crops. Wheat leaf diseases, though, severely hamper development. The quality of wheat and the agricultural economy rely on prompt and correct identification of wheat leaf diseases. This work propose an integrated machine learning technique, According to the results of this study, when compared to other models, the Fuzzy Opponent Histogram Filter that makes use of Bayes Net performs the best. The Fuzzy Opponent Histogram Filter with Bayes Net gives best outcome which is 97% of accuracy.

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“…x − X min X max − X min (6) where x is the pixel value of the original image, X min is the minimum value of the pixel value set, and X max represents the maximum value of the pixel value set.…”

Section: Ip102 Datasetmentioning

confidence: 99%

“…With the development of computer vision technology, computer computing power, and various algorithms of artificial intelligence (AI) [1,2], machine learning [3,4], and modern digital and deep learning [5], many crop pest detection and recognition methods have been presented [6]. Martineau et al [7] investigated forty-four studies on this topic, including a lot of methods of image capture, feature extraction, and classification and tested datasets, and generally discussed the questions that might still remain unsolved.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Convolution-Capsule Network for Crop Insect Pest Recognition

Zhang

et al. 2022

Electronics

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Accurate crop insect pest identification in fields is useful to control pests and beneficial to agricultural yield and quality. However, it is a difficult and challenging problem due to the crop insect pests being small with various sizes, postures, shapes, and disorganized backgrounds. Multi-scale convolution-capsule network (MSCCN) is constructed for crop insect pest identification. It consists of a multi-scale convolution module, capsule network (CapsNet) module, and SoftMax classification module. Multi-scale convolution is used to extract the multi-scale discriminative features, CapsNet is employed to encode the hierarchical structure of the size-variant insect pests in the crop images, and Softmax is adopted for insect pest identification. MSCCN combines the advantages of convolutional neural network (CNN), CapsNet, and multi-scale CNN, and can learn multi-scale robust features from pest images of different shapes and sizes for pest recognition and identify various morphed pests. Experimental results on the crop pest image dataset show that this method has a good recognition rate of 91.4%.

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Application of agricultural insect pest detection and control map based on image processing analysis

Cited by 17 publications

References 11 publications

Detection of maize stem diameter by using RGB-D cameras’ depth information under selected field condition

Detection of maize stem diameter by using RGB-D cameras’ depth information under selected field condition

The Novel ML Approaches on Wheat Disease by CLF and FOHF of Image Equalization Techniques

Multi-Scale Convolution-Capsule Network for Crop Insect Pest Recognition

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