A Lightweight Mobile System for Crop Disease Diagnosis

Siricharoen, Punnarai; Scotney, Bryan; Morrow, Philip; Parr, Gerard

doi:10.1007/978-3-319-41501-7_87

Cited by 23 publications

(11 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In such areas, internet penetration, smartphone and unmanned aerial vehicle (UAV) technologies offer new tools for in-field plant disease detection based on automated image recognition that can aid in early detection at a large scale. Previous research has demonstrated automated image recognition of crop diseases in wheat (Gibson et al, 2015 ; Siricharoen et al, 2016 ), apples (Dubey and Jalal, 2014 ) and on datasets of healthy and diseased plants (Mohanty et al, 2016 ); this technology was also demonstrated on UAVs (Puig et al, 2015 ). Cassava disease detection based on automated image recognition through feature extraction has shown promising results (Aduwo et al, 2010 ; Abdullakasim et al, 2011 ; Mwebaze and Owomugisha, 2016 ) but extracting features is computationally intensive and requires expert knowledge for robust performance.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Image-Based Cassava Disease Detection

et al. 2017

View full text Add to dashboard Cite

Cassava is the third largest source of carbohydrates for human food in the world but is vulnerable to virus diseases, which threaten to destabilize food security in sub-Saharan Africa. Novel methods of cassava disease detection are needed to support improved control which will prevent this crisis. Image recognition offers both a cost effective and scalable technology for disease detection. New deep learning models offer an avenue for this technology to be easily deployed on mobile devices. Using a dataset of cassava disease images taken in the field in Tanzania, we applied transfer learning to train a deep convolutional neural network to identify three diseases and two types of pest damage (or lack thereof). The best trained model accuracies were 98% for brown leaf spot (BLS), 96% for red mite damage (RMD), 95% for green mite damage (GMD), 98% for cassava brown streak disease (CBSD), and 96% for cassava mosaic disease (CMD). The best model achieved an overall accuracy of 93% for data not used in the training process. Our results show that the transfer learning approach for image recognition of field images offers a fast, affordable, and easily deployable strategy for digital plant disease detection.

show abstract

Section: Introductionmentioning

confidence: 99%

Deep Learning for Image-Based Cassava Disease Detection

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Other works like Xie et al (2016) focused on reducing the computational complexity of the automation algorithm with relative success. In 2016, Siricharoen et al (2016) developed a technique that combined texture, color and shape to detect the presence of a specific disease on a plant. From 2014 to 2016, Johannes et al…”

Section: Related Workmentioning

confidence: 99%

Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild

Picón

Alvarez-Gila

Seitz

et al. 2019

Computers and Electronics in Agriculture

344

147

View full text Add to dashboard Cite

Fungal infection represents up to 50% of yield losses, making it necessary to apply effective and cost efficient fungicide treatments, whose efficacy depends on infestation type, situation and time. In these cases, a correct and early identification of the specific infection is mandatory to minimize yield losses and increase the efficacy and efficiency of the treatments. Over the last years, a number of image analysis-based methodologies have been proposed for automatic image disease identification. Among these methods, the use of Deep Convolutional Neural Networks (CNNs) has proven tremendously successful for different visual classification tasks. In this work we extend previous work by Johannes et al. (2017) with an adapted Deep Residual Neural Network-based algorithm to deal with the detection of multiple plant diseases in real acquisition conditions where different adaptions for early disease detection have been proposed. This work analyses the performance of early identification of three relevant European endemic wheat diseases: Septoria (Septoria triciti ), Tan Spot (Drechslera triciti-repentis) and Rust (Puccinia striiformis & Puccinia recondita). The analysis was done using

show abstract

“…Sannakki et al [25] proposed an automatic infected region identification technique based on k‐means clustering, by clustering the infected and healthy pixels. Siricharoen et al [87] identified diseases by combining various features but did not emphasise on early symptom generations. Johannes et al [88] developed a technique for automated diagnosis system for the detection of rust, septoria, and tan spots in wheat crop using a mobile device.…”

Section: Categorical Classification Of Algorithmic Techniquesmentioning

confidence: 99%