Detection of diseases and pests on images captured in uncontrolled conditions from tea plantations

Bhatt, Prakruti; Sarangi, Sanat; Pappula, Srinivasu

doi:10.1117/12.2518868

Cited by 21 publications

(10 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the performance evaluation of object detection problems, it is observed that different performance evaluation criteria are used in challenges such as PASCAL VOC, ImageNet and COCO. In this study, AP (average precision) score which is commonly used in the literature [6,11,23] as a performance evaluation criterion in the detection of plant diseases has been employed.…”

Section: Findings and Discussionmentioning

confidence: 99%

“…Findings with regards to accuracy and processing time were obtained in the segmentation and detection of foreign objects. Accordingly, an accuracy performance score of 95% was obtained in the object segmentation and detection processes carried out in durations less than 50 ms. Bhatt et al [11] developed a YOLO architecture based disease and pest detection application for a data set of tea plants created under uncontrolled conditions. As a result of the study, a mAP score of 0.86 was obtained.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Olive Peacock Spot Disease Recognition System Development by Using Single Shot Detector

Uğuz

2020

Sakarya University Journal of Computer and Information Sciences

View full text Add to dashboard Cite

Among the artificial intelligence based studies conducted in the field of agriculture, disease recognition methods founded on deep learning are observed to become widespread. Due to the diversity and regional specificity of many plant species, studies performed in this field are not at the desired level. Olive peacock spot disease of the olive plant which grows only in certain regions in the world is a widely encountered disease particularly in Turkey. The aim of this research is to develop an olive peacock spot disease detection system using a Single Shot Detector (SSD) which is one the popular deep learning architectures to support olive farmers. This study presents a data set consisting of 1460 olive leaves samples for the detection of olive peacock spot disease. All of the images of the olive leaves which produced under controlled conditions were collected from Aegean region of Turkey during spring and summer. The data set was trained with different intersection over union (IoU) threshold values using SSD architecture. A 96% average precision (AP) value was obtained with IoU=0.5. As IOU value goes up from 0.5, erroneously classified olive peacock spot disease symptoms growed larger as well. The AP curve becomes flat when between 0.1 and 0.5, and it decreases when greater than 0.5. This analysis showed that the IoU significantly influenced the performance of SSD based model in detection of olive peacock spot disease. In addition to, trainings were performed by employing Pytorch library and a GUI was developed for the SSD based application using PyQt5 which is one of Pyhton's libraries. Results showed that the SSD was a robust tool for recognizing the olive peacock spot disease.

show abstract

Section: Findings and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Olive Peacock Spot Disease Recognition System Development by Using Single Shot Detector

Uğuz

2020

Sakarya University Journal of Computer and Information Sciences

View full text Add to dashboard Cite

show abstract

“…The FPS of the new model has also improved (from 24 to 28.4), reaching the standard of real-time detection. Bhatt et al (2019) presented a method to detect pests and diseases on images captured under uncontrolled conditions in tea gardens. YOLOv3 was used to detect pests and diseases.…”

Section: Related Workmentioning

confidence: 99%

Multiscale Parallel Algorithm for Early Detection of Tomato Gray Mold in a Complex Natural Environment

Wang

2021

Front. Plant Sci.

View full text Add to dashboard Cite

Plant disease detection technology is an important part of the intelligent agricultural Internet of Things monitoring system. The real natural environment requires the plant disease detection system to have extremely high real time detection and accuracy. The lightweight network MobileNetv2-YOLOv3 model can meet the real-time detection, but the accuracy is not enough to meet the actual needs. This study proposed a multiscale parallel algorithm MP-YOLOv3 based on the MobileNetv2-YOLOv3 model. The proposed method put forward a multiscale feature fusion method, and an efficient channel attention mechanism was introduced into the detection layer of the network to achieve feature enhancement. The parallel detection algorithm was used to effectively improve the detection performance of multiscale tomato gray mold lesions while ensuring the real-time performance of the algorithm. The experimental results show that the proposed algorithm can accurately and real-time detect multiscale tomato gray mold lesions in a real natural environment. The F1 score and the average precision reached 95.6 and 93.4% on the self-built tomato gray mold detection dataset. The model size was only 16.9 MB, and the detection time of each image was 0.022 s.

show abstract

“…Another popular branch is the recognition of Plant Disease. For instance, [6] and [7], regardless of their namely relation of pest identification, they actually do not consider of pest damages on tea plantations and tomato separately.…”

Section: Introductionmentioning

confidence: 99%

Computer Vision and Deep Learning for Precise Agriculture: A Case Study of Lemon Leaf Image Classification

Yuan

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Crop protection, an crucial field of precise agriculture, requires attention and improvement, as it secures sustainability and safety of crop and food production. There are various threats to crops in which pest is one of the severest. Computer vision technologies based on deep learning have shown great advantages on image classification as they enable real-time pest recognition on devices with cameras, such as drones. Thus, it is promising for pest monitoring and control and many DL models have been developed. Furthermore, early and accurate diagnosis is need as it minimizes pest damage. However, traditional models are limited on speed because the massive parameters require huge computing resource. In this work, we investigate the capability of lightweight model based on DL for the task of leaf disease classification on uncontrolled environment and compare it with traditional DL model. Lightweight models, in general, are designed to reduce computation on convolution layers with acceptable accuracy lose. We use an open database named LeLePhid, which contains lemon leave images, healthy or affected by aphid. The damage caused by aphid is general as the pest makes obvious changes to leaf outlooks. We focus on two typical DL models: the traditional, DenseNet and the lightweight, MobileNet, and discuss the balance between speed and accuracy, in order to support real-time analytics. Finally, we discuss the challenges and opportunities in practice.

show abstract

Detection of diseases and pests on images captured in uncontrolled conditions from tea plantations

Cited by 21 publications

References 10 publications

Automatic Olive Peacock Spot Disease Recognition System Development by Using Single Shot Detector

Automatic Olive Peacock Spot Disease Recognition System Development by Using Single Shot Detector

Multiscale Parallel Algorithm for Early Detection of Tomato Gray Mold in a Complex Natural Environment

Computer Vision and Deep Learning for Precise Agriculture: A Case Study of Lemon Leaf Image Classification

Contact Info

Product

Resources

About