Deep learning-based identification system of weeds and crops in strawberry and pea fields for a precision agriculture sprayer

Khan, Shahbaz; Tufail, Muhammad; Khan, Muhammad Tahir; Khan, Zulfiqar Ahmad; Anwar, Shahzad

doi:10.1007/s11119-021-09808-9

Cited by 91 publications

(43 citation statements)

References 40 publications

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“…New information and communication technology enable new options for active tracking of farmland, cattle, and water infrastructure, with the ultimate goal of reducing living beings. Among the most significant aspects of precision farming are managing resources [17]. Making better use of resources encourages better performance.…”

Section: Precision Agriculturementioning

confidence: 99%

Application of IoT-Based Drones in Precision Agriculture for Pest Control

Refaai

Dattu

Gireesh³

et al. 2022

Advances in Materials Science and Engineering

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Unmanned aerial vehicles (UAVs), commonly known as drones, have been progressively prevalent due to their capability to operate quickly and their vast range of applications in a variety of real-world circumstances. The utilization of UAVs in precision farming has lately gained a lot of attention from the scientific community. This study addresses with the assistance of drones in the precision agricultural area. This paper makes significant contributions by analyzing communication protocols and applying them to the challenge of commanding a fleet of drones to protect crops from parasite infestations. In this research, the effectiveness of nine powerful deep neural network models is measured for the detection of plant diseases using diverse methodologies. These deep neural networks are adapted to the immediate situation using transfer learning and deep extraction of features approaches. The presented study takes into account the used pretrained deep learning model for extracting features and fine-tuning. The deep feature extraction characteristics are subsequently categorized using support vector machines (SVMs) and extreme learning machines (ELMs). For measuring performance, the precision, sensitivities, specific, and F1-score are all evaluated. Deep feature extraction and SVM/ELM classification generated better outcomes than transfer learning, according to the analysis result. Furthermore, the analysis of the various methodologies tries to assess their effectiveness and costs. The different approaches, for example, confront difficulties such as investigating the region in the shortest possible time feasible, while eliminating the same region being searched by more drones, detecting parasites, and stopping their spread by applying the appropriate number of pesticides. Simulation models are a significant aid to researchers in conducting to evaluate these technologies and creating specific tactics and coordinating procedures capable of effectively supporting farms and achieving the aim. The main objective of this paper is to compare the search techniques of two distinct methods of parasitic to identify performance.

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Section: Precision Agriculturementioning

confidence: 99%

Application of IoT-Based Drones in Precision Agriculture for Pest Control

Refaai

Dattu

Gireesh³

et al. 2022

Advances in Materials Science and Engineering

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“…A UAV was used to detect weeds using Faster RCNN and SSD models [23]. An article presented an improved version of the Faster RCNN model for the image collected by UAV for weed identification [24]. The performance of the proposed method was compared with other DL and ML methods.…”

Section: Introductionmentioning

confidence: 99%

Weed Detection by Faster RCNN Model: An Enhanced Anchor Box Approach

2022

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To apply weed control treatments effectively, the weeds must be accurately detected. Deep learning (DL) has been quite successful in performing the weed identification task. However, various aspects of the DL have not been explored in previous studies. This research aimed to achieve a high average precision (AP) of eight classes of weeds and a negative (non-weed) class, using the DeepWeeds dataset. In this regard, a DL-based two-step methodology has been proposed. This article is the second stage of the research, while the first stage has already been published. The former phase presented a weed detection pipeline and consisted of the evaluation of various neural networks, image resizers, and weight optimization techniques. Although a significant improvement in the mean average precision (mAP) was attained. However, the Chinee apple weed did not reach a high average precision. This result provided a solid ground for the next stage of the study. Hence, this paper presents an in-depth analysis of the Faster Region-based Convolutional Neural Network (RCNN) with ResNet-101, the best-obtained model in the past step. The architectural details of the Faster RCNN model have been thoroughly studied to investigate each class of weeds. It was empirically found that the generation of anchor boxes affects the training and testing performance of the Faster RCNN model. An enhancement to the anchor box scales and aspect ratios has been attempted by various combinations. The final results, with the addition of 64 × 64 scale size, and aspect ratio of 1:3 and 3:1, produced the best classification and localization of all classes of weeds and a negative class. An enhancement of 24.95% AP was obtained in Chinee apple weed. Furthermore, the mAP was improved by 2.58%. The robustness of the approach has been shown by the stratified k-fold cross-validation technique and testing on an external dataset.

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“…Weed suppression is one of the greatest factors affecting crop production. The weeds could compete with crops for water, light, fertilizer, growth space, other nutrients, etc ., resulting in reduction of crop yield and production quality ( Khan et al., 2021 ). It could also be the host of many pathogens and insects, which would damage crop plants.…”

Section: Introductionmentioning

confidence: 99%

Weed25: A deep learning dataset for weed identification

et al. 2022

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Weed suppression is an important factor affecting crop yields. Precise identification of weed species will contribute to automatic weeding by applying proper herbicides, hoeing position determination, and hoeing depth to specific plants as well as reducing crop injury. However, the lack of datasets of weeds in the field has limited the application of deep learning techniques in weed management. In this paper, it presented a dataset of weeds in fields, Weed25, which contained 14,035 images of 25 different weed species. Both monocot and dicot weed image resources were included in this dataset. Meanwhile, weed images at different growth stages were also recorded. Several common deep learning detection models—YOLOv3, YOLOv5, and Faster R-CNN—were applied for weed identification model training using this dataset. The results showed that the average accuracy of detection under the same training parameters were 91.8%, 92.4%, and 92.15% respectively. It presented that Weed25 could be a potential effective training resource for further development of in-field real-time weed identification models. The dataset is available at https://pan.baidu.com/s/1rnUoDm7IxxmX1n1LmtXNXw; the password is rn5h.

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Deep learning-based identification system of weeds and crops in strawberry and pea fields for a precision agriculture sprayer

Cited by 91 publications

References 40 publications

Application of IoT-Based Drones in Precision Agriculture for Pest Control

Application of IoT-Based Drones in Precision Agriculture for Pest Control

Weed Detection by Faster RCNN Model: An Enhanced Anchor Box Approach

Weed25: A deep learning dataset for weed identification

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