Deep Learning for Real-Time Diagnosis of Pest and Diseases on Crops

Gambhir, Jinendra; Patel, Naveen; Patil, Shrinivas; Takale, Prathamesh; Chougule, Archana; Prabhakar, Chandra S.; Managanvi, Kalmesh; Raghavan, Aswin; Sohane, R. K.

doi:10.1007/978-981-16-6624-7_19

Cited by 7 publications

(6 citation statements)

References 5 publications

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“…Li et al [36] developed a real-time plant disease and pest identification system on video using faster R-CNN as an object detection framework. Gambhir et al [37] designed a CNN-based dynamic android and web UI for agricultural pest and disease detection. The findings suggested that the proposed method could identify previously unseen rice diseases on video.…”

Section: Related Workmentioning

confidence: 99%

Large-Scale Insect Detection With Fine-Tuning YOLOX

Doan

2023

ijmst

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With the aim of detecting insect pests at an early stage, there has been an increasing demand for insect pest detection and classification, particularly in large-scale setups. Therefore, the aim of this research is to introduce a new real-time pest detection technique using a deep convolutional neural network, which not only offers improved accuracy but also faster speed and less computational effort. The networks were constructed using various modern object detector models such as YOLOv4, YOLOv5, and YOLOX. Our proposed networks were evaluated on a standard large-scale insect pest dataset, IP102, as well as on our collected dataset, Insect10. The experimental results demonstrate that our system surpasses previous methods and achieves satisfactory performance with 84.84% mAP on the Insect10 dataset and 54.19% mAP on the IP102 dataset. Our system can deliver precise and real-time pest detection and identification for agricultural crops, enabling highly accurate end-to-end pest detection that can be applied in realistic farming scenarios.

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Section: Related Workmentioning

confidence: 99%

Large-Scale Insect Detection With Fine-Tuning YOLOX

Doan

2023

ijmst

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“…In addition, Koklu et al developed a deep feature based on CNN-SVM to classify 5 species of grapevine leaves, in which a classification accuracy of 97.60% was achieved [27]. Gambhir et al developed a CNN-based interactive android and web interface for the diagnosis of pests and diseases on crops [28]. Li et al developed a real-time plant disease and pest recognition system on video through faster R-CNN as an object detection framework [22].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Based Detector YOLOv5 for Identifying Insect Pests

et al. 2022

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Insect pests are a major element influencing agricultural production. According to the Food and Agriculture Organization (FAO), an estimated 20–40% of pest damage occurs each year, which reduces global production and becomes a major challenge to crop production. These insect pests cause sooty mold disease by sucking the sap from the crop’s organs, especially leaves, fruits, stems, and roots. To control these pests, pesticides are frequently used because they are fast-acting and scalable. Due to environmental pollution and health awareness, less use of pesticides is recommended. One of the salient approaches could be to reduce the wide use of pesticides by spraying on demand. To perform spot spraying, the location of the pest must first be determined. Therefore, the growing population and increasing food demand emphasize the development of novel methods and systems for agricultural production to address environmental concerns and ensure efficiency and sustainability. To accurately identify these insect pests at an early stage, insect pest detection and classification have recently become in high demand. Thus, this study aims to develop an object recognition system for the detection of crops damaging insect pests and their classification. The current work proposes an automatic system in the form of a smartphone IP- camera to detect insect pests from digital images/videos to reduce farmers’ reliance on pesticides. The proposed approach is based on YOLO object detection architectures including YOLOv5 (n, s, m, l, and x), YOLOv3, YOLO-Lite, and YOLOR. For this purpose, we collected 7046 images in the wild under different illumination and background conditions to train the underlying object detection approaches. We trained and test the object recognition system with different parameters from scratch. The eight models are compared and analyzed. The experimental results show that the average precision (AP@0.5) of the eight models including YOLO-Lite, YOLOv3, YOLOR, and YOLOv5 with five different scales (n, s, m, l, and x) reach 51.7%, 97.6%, 96.80%, 83.85%, 94.61%, 97.18%, 97.04%, and 98.3% respectively. The larger the model, the higher the average accuracy of the detection validation results. We observed that the YOLOv5x model is fully functional and can correctly identify the twenty-three species of insect pests at 40.5 milliseconds (ms). The developed model YOLOv5x performs the state-of-the-art model with an average precision value of (mAP@0.5) 98.3%, (mAP@0.5:0.95) value of 79.8%, precision of 94.5% and a recall of 97.8%, and F1-score with 96% on our IP-23 dataset. The results show that the system works efficiently and was able to correctly detect and identify insect pests, which can be employed for realistic application while farming.

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“…Sabanci et al proposed a convolutional recurrent hybrid network combining AlexNet and BiLSTM for the detection of pest-damaged wheat [ 12 ]. Gambhir et al developed a CNN-based interactive network robot to diagnose pests and diseases on crops [ 13 ]. Sun et al proposed a multi-scale feature fusion instance detection method based on SSD, which improved on SSD to detect maize leaf blight in complex backgrounds [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Maize-YOLO: A New High-Precision and Real-Time Method for Maize Pest Detection

Yang

Xing

Wang

et al. 2023

Insects

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The frequent occurrence of crop pests and diseases is one of the important factors leading to the reduction of crop quality and yield. Since pests are characterized by high similarity and fast movement, this poses a challenge for artificial intelligence techniques to identify pests in a timely and accurate manner. Therefore, we propose a new high-precision and real-time method for maize pest detection, Maize-YOLO. The network is based on YOLOv7 with the insertion of the CSPResNeXt-50 module and VoVGSCSP module. It can improve network detection accuracy and detection speed while reducing the computational effort of the model. We evaluated the performance of Maize-YOLO in a typical large-scale pest dataset IP102. We trained and tested against those pest species that are more damaging to maize, including 4533 images and 13 classes. The experimental results show that our method outperforms the current state-of-the-art YOLO family of object detection algorithms and achieves suitable performance at 76.3% mAP and 77.3% recall. The method can provide accurate and real-time pest detection and identification for maize crops, enabling highly accurate end-to-end pest detection.

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Deep Learning for Real-Time Diagnosis of Pest and Diseases on Crops

Cited by 7 publications

References 5 publications

Large-Scale Insect Detection With Fine-Tuning YOLOX

Large-Scale Insect Detection With Fine-Tuning YOLOX

Deep Learning Based Detector YOLOv5 for Identifying Insect Pests

Maize-YOLO: A New High-Precision and Real-Time Method for Maize Pest Detection

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