Leaf Disease Segmentation and Detection in Apple Orchards for Precise Smart Spraying in Sustainable Agriculture

Storey, Gary; Meng, Qinggang; Li, Baihua

doi:10.3390/su14031458

Cited by 55 publications

(18 citation statements)

References 39 publications

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“…However, the authors focus on diagnosing the severity of the disease present on the apple leaf. Other authors [ 34 ] have proposed improvements in disease detection in apple crops using segmentation and disease detection. Segmentation is typically used to locate objects and shapes in images and refers to the process of dividing a digital image into multiple regions or objects.…”

Section: Literature Reviewmentioning

confidence: 99%

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: Literature Reviewmentioning

confidence: 99%

Smart Strawberry Farming Using Edge Computing and IoT

Cruz

Mafra

Teixeira

et al. 2022

Sensors

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“…This Depending on the approach utilized, various annotation types are used to annotate images. The popular image annotation techniques employed in agriculture based on deep learning are bounding box [83][84][85][86] and segmentation [87][88][89][90]. The study in [91] proposed the tools to boost the efficiency of identifying agriculture images, which frequently have more various objects and more detailed shapes than those in many general datasets.…”

Section: Deep Learning For Image Annotationmentioning

confidence: 99%

Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

et al. 2022

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The implementation of intelligent technology in agriculture is seriously investigated as a way to increase agriculture production while reducing the amount of human labor. In agriculture, recent technology has seen image annotation utilizing deep learning techniques. Due to the rapid development of image data, image annotation has gained a lot of attention. The use of deep learning in image annotation can extract features from images and has been shown to analyze enormous amounts of data successfully. Deep learning is a type of machine learning method inspired by the structure of the human brain and based on artificial neural network concepts. Through training phases that can label a massive amount of data and connect them up with their corresponding characteristics, deep learning can conclude unlabeled data in image processing. For complicated and ambiguous situations, deep learning technology provides accurate predictions. This technology strives to improve productivity, quality and economy and minimize deficiency rates in the agriculture industry. As a result, this article discusses the application of image annotation in the agriculture industry utilizing several deep learning approaches. Various types of annotations that were used to train the images are presented. Recent publications have been reviewed on the basis of their application of deep learning with current advancement technology. Plant recognition, disease detection, counting, classification and yield estimation are among the many advancements of deep learning architecture employed in many applications in agriculture that are thoroughly investigated. Furthermore, this review helps to assist researchers to gain a deeper understanding and future application of deep learning in agriculture. According to all of the articles, the deep learning technique has successfully created significant accuracy and prediction in the model utilized. Finally, the existing challenges and future promises of deep learning in agriculture are discussed.

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“…Finally, in [4], authors labeled with bounding boxes 5,000 images of tomato diseases. Recently, a dataset for Apple Orchards diseases has been proposed in [12], which contains handmade annotated segmentation of a image subset (142) from Plant Pathology Challenge 2020 database [13]. The [14] is an example of a dataset for instance segmentation for cotton leaf disease detection, with 2,000 photos made with a smartphone camera in a real-world scenario.…”

Section: Related Workmentioning

confidence: 99%

LDD: A Dataset for Grape Diseases Object Detection and Instance Segmentation

Rossi,

Valenti,

Legler

et al. 2022

Preprint

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The Instance Segmentation task, an extension of the wellknown Object Detection task, is of great help in many areas, such as precision agriculture: being able to automatically identify plant organs and the possible diseases associated with them, allows to effectively scale and automate crop monitoring and its diseases control. To address the problem related to early disease detection and diagnosis on vines plants, a new dataset has been created with the goal of advancing the stateof-the-art of diseases recognition via instance segmentation approaches. This was achieved by gathering images of leaves and clusters of grapes affected by diseases in their natural context. The dataset contains photos of 10 object types which include leaves and grapes with and without symptoms of the eight more common grape diseases, with a total of 17,706 labeled instances in 1,092 images. Multiple statistical measures are proposed in order to offer a complete view on the characteristics of the dataset. Preliminary results for the object detection and instance segmentation tasks reached by the models Mask R-CNN [6] and R 3 -CNN [10] are provided as baseline, demonstrating that the procedure is able to reach promising results about the objective of automatic diseases' symptoms recognition.

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Leaf Disease Segmentation and Detection in Apple Orchards for Precise Smart Spraying in Sustainable Agriculture

Cited by 55 publications

References 39 publications

Smart Strawberry Farming Using Edge Computing and IoT

Smart Strawberry Farming Using Edge Computing and IoT

Advanced Technology in Agriculture Industry by Implementing Image Annotation Technique and Deep Learning Approach: A Review

LDD: A Dataset for Grape Diseases Object Detection and Instance Segmentation

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