An Instance Segmentation Model for Strawberry Diseases Based on Mask R-CNN

Afzaal, Usman; Bhattarai, Bhuwan; Pandeya, Yagya Raj; Lee, Joonwhoan

doi:10.3390/s21196565

Cited by 69 publications

(39 citation statements)

References 63 publications

(60 reference statements)

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“…The final deployed model can detect and classify seven of the most common diseases in strawberries. Figure 9 [ 42 ] illustrates in more detail the presence of these diseases in strawberries. These diseases are described as follows: Angular Leaf Spot : Known as “bacterial stain”, the disease is caused by Xanthomonas fragariae bacteria.…”

Section: Proposed Iot Plataformmentioning

confidence: 99%

“…For training purposes, a dataset with 2500 images is used to train and test the detection model proposed. The dataset founded on the Kaggle community website is created, labeled, and offered by members of the AI lab, Computer Science and Engineering department, JBNU [ 42 ] and has images of strawberries with seven major and most common diseases: Angular Leaf Spot, Anthracnose Fruit Rot, Blossom Blight, Grey Mold, Leaf Spot, Powdery Mildew Fruit, and Powdery Mildew Leaf. The 2500 images are split into 80% for training and 20% for the testing phase.…”

Section: Proposed Iot Plataformmentioning

confidence: 99%

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Smart Strawberry Farming Using Edge Computing and IoT

Cruz

Mafra

Teixeira

et al. 2022

Sensors

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Strawberries are sensitive fruits that are afflicted by various pests and diseases. Therefore, there is an intense use of agrochemicals and pesticides during production. Due to their sensitivity, temperatures or humidity at extreme levels can cause various damages to the plantation and to the quality of the fruit. To mitigate the problem, this study developed an edge technology capable of handling the collection, analysis, prediction, and detection of heterogeneous data in strawberry farming. The proposed IoT platform integrates various monitoring services into one common platform for digital farming. The system connects and manages Internet of Things (IoT) devices to analyze environmental and crop information. In addition, a computer vision model using Yolo v5 architecture searches for seven of the most common strawberry diseases in real time. This model supports efficient disease detection with 92% accuracy. Moreover, the system supports LoRa communication for transmitting data between the nodes at long distances. In addition, the IoT platform integrates machine learning capabilities for capturing outliers in collected data, ensuring reliable information for the user. All these technologies are unified to mitigate the disease problem and the environmental damage on the plantation. The proposed system is verified through implementation and tested on a strawberry farm, where the capabilities were analyzed and assessed.

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Section: Proposed Iot Plataformmentioning

confidence: 99%

Section: Proposed Iot Plataformmentioning

confidence: 99%

Smart Strawberry Farming Using Edge Computing and IoT

Cruz

Mafra

Teixeira

et al. 2022

Sensors

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show abstract

“…Afzaal et al [100] Other crops are also very susceptible to various diseases, which has led to an increase in the world of agriculture and industry. To improve the quality of plants, plants must protect plants from all kinds of harmful diseases.…”

Section: G Disease Controlmentioning

confidence: 99%

A Systematic Review of Current Trends in Artificial Intelligence for Smart Farming to Enhance Crop Yield

Widianto

Ardimansyah²,

Pohan³

et al. 2022

Journal of Robotics and Control

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Current technology has been widely applied for development, one of which has an Artificial Intelligence (AI) applied to Smart Farming. AI can give special capabilities to be programmed as needed. In cooperation with agricultural systems, AI is part of improving the quality of agriculture. This technology is no stranger to being applied in basic fields such as agriculture. This smart technology is needed to increase crop yields for various regions by utilizing the current trends paper. This is necessary because less land is available for agriculture, and there is a greater need for food sources. Therefore, this systematic review aims to collect the current trends in AI studies for Smart Farming papers using the latest year features from 2018-2022. This paper is handy for researchers and industry in looking for the latest papers on research to enhance crop yields. The authors utilized Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) of 534 articles from IEEE, ACM, MDPI, IAES, and ScienceDirect. After going through a careful process, 67 papers were found that were judged according to the criteria. After the authors got some of the current trends, the author has discussed several factors regarding the results obtained to enhance crop yields, such as Weather, Soil, Irrigation, Unmanned Aerial Vehicle (UAV), Pest Control, Weed Control, and Disease Control.

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“…A bounding box presents an object's location. Based on [16], the object detection model includes two types: a one-stage method and a two-stage method. Onestage models consist of YOLO [17], Efficient Det [18], and CenterNet [19].…”

Section: Introductionmentioning

confidence: 99%

“…There is a growing interest in the research of Mask R-CNN as well as various applications. In [16], strawberry diseases are detected with low cost and good accuracy. In [25], in order to count the plants and calculate the size of plants, the image taken by a drone is processed by Mask R-CNN.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Mask R-CNN for Solid-Volume Food Instance Segmentation and Calorie Estimation

2022

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To prevent or deal with chronic diseases, using a smart device, automatically classifying food categories, estimating food volume and nutrients, and recording dietary intake are considered challenges. In this work, a novel real-time vision-based method for solid-volume food instance segmentation and calorie estimation is utilized, based on Mask R-CNN. In order to address the proposed method in real life, distinguishing it from other methods which use 3D LiDARs or RGB-D cameras, this work applies RGB images to train the model and uses a simple monocular camera to test the result. Gimbap is selected as an example of solid-volume food to show the utilization of the proposed method. Firstly, in order to improve detection accuracy, the proposed labeling approach for the Gimbap image datasets is introduced, based on the posture of Gimbap in plates. Secondly, an optimized model to detect Gimbap is created by fine-tuning Mask R-CNN architecture. After training, the model reaches AP (0.5 IoU) of 88.13% for Gimbap1 and AP (0.5 IoU) of 82.72% for Gimbap2. mAP (0.5 IoU) of 85.43% is achieved. Thirdly, a novel calorie estimation approach is proposed, combining the calibration result and the Gimbap instance segmentation result. In the fourth section, it is also shown how to extend the calorie estimation approach to be used in any solid-volume food, such as pizza, cake, burger, fried shrimp, oranges, and donuts. Compared with other food calorie estimation methods based on Faster R-CNN, the proposed method uses mask information and considers unseen food. Therefore, the method in this paper outperforms the accuracy of food segmentation and calorie estimation. The effectiveness of the proposed approaches is proven.

show abstract

An Instance Segmentation Model for Strawberry Diseases Based on Mask R-CNN

Cited by 69 publications

References 63 publications

Smart Strawberry Farming Using Edge Computing and IoT

Smart Strawberry Farming Using Edge Computing and IoT

A Systematic Review of Current Trends in Artificial Intelligence for Smart Farming to Enhance Crop Yield

Utilizing Mask R-CNN for Solid-Volume Food Instance Segmentation and Calorie Estimation

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