Mobile Application for Classification of Plant Leaf Diseases Using Image Processing and Neural Networks

Chethan, K S; Donepudi, Sumanth; Supreeth, H. V.; Maani, Vachan D.

doi:10.1007/978-981-15-8530-2_22

Cited by 8 publications

(7 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although previous studies have explored the utilization of DL networks and the development of basic mobile application prototypes for plant image classification [49,50], Figure 6 illustrates the applications from a user perspective. The manager with data management capability will add, edit, and deactivate any users, report wrong data, and so on through the HTTP web services.…”

Section: System Flow With Sequence Diagrammentioning

confidence: 99%

“…Although previous studies have explored the utilization of DL networks and the development of basic mobile application prototypes for plant image classification [49,50], our methodology introduces a distinctive viewpoint. Our research's investigative goals, the specific neural networks selected, and the evaluation criteria for our models, along with our emphasis on constructing a mobile-friendly model capable of identifying coffee leaf disease classes in a robust multi-label classification system, collectively represent an innovative approach in our field.…”

Section: System Flow With Sequence Diagrammentioning

confidence: 99%

See 1 more Smart Citation

Revolutionizing Coffee Farming: A Mobile App with GPS-Enabled Reporting for Rapid and Accurate On-Site Detection of Coffee Leaf Diseases Using Integrated Deep Learning

Hitimana,

Kuradusenge,

Sinayobye

et al. 2024

Software

View full text Add to dashboard Cite

Coffee leaf diseases are a significant challenge for coffee cultivation. They can reduce yields, impact bean quality, and necessitate costly disease management efforts. Manual monitoring is labor-intensive and time-consuming. This research introduces a pioneering mobile application equipped with global positioning system (GPS)-enabled reporting capabilities for on-site coffee leaf disease detection. The application integrates advanced deep learning (DL) techniques to empower farmers and agronomists with a rapid and accurate tool for identifying and managing coffee plant health. Leveraging the ubiquity of mobile devices, the app enables users to capture high-resolution images of coffee leaves directly in the field. These images are then processed in real-time using a pre-trained DL model optimized for efficient disease classification. Five models, Xception, ResNet50, Inception-v3, VGG16, and DenseNet, were experimented with on the dataset. All models showed promising performance; however, DenseNet proved to have high scores on all four-leaf classes with a training accuracy of 99.57%. The inclusion of GPS functionality allows precise geotagging of each captured image, providing valuable location-specific information. Through extensive experimentation and validation, the app demonstrates impressive accuracy rates in disease classification. The results indicate the potential of this technology to revolutionize coffee farming practices, leading to improved crop yield and overall plant health.

show abstract

Section: System Flow With Sequence Diagrammentioning

confidence: 99%

Section: System Flow With Sequence Diagrammentioning

confidence: 99%

Revolutionizing Coffee Farming: A Mobile App with GPS-Enabled Reporting for Rapid and Accurate On-Site Detection of Coffee Leaf Diseases Using Integrated Deep Learning

Hitimana,

Kuradusenge,

Sinayobye

et al. 2024

Software

View full text Add to dashboard Cite

show abstract

“…The comparative study shows that many features together give more accurate values than features taken as single types. Chethan et al [22] uses the advanced histogram equalization technique to preprocess the images and then using k-means clustering the images are segmented. Features are extracted with the help of a grey-level co-occurrence matrix.…”

Section: Literature Reviewmentioning

confidence: 99%

Preprocessing of leaf images using brightness preserving dynamic fuzzy histogram equalization technique

John

Joseph

2023

IJ-AI

View full text Add to dashboard Cite

Agriculture serves as the backbone of many countries. It provides food and other essential materials as per our requirement. Various kinds of diseases are affecting the agricultural crops which in turn reduce the quantity and quality of the agricultural sector. This can also lead to the decrease in food production thereby affecting the economic growth and development. Even though the symptoms and other impacts of the diseases are outwardly visible, manual identification of diseases and rectification is a tedious and time-consuming process. Therefore, detecting the diseases using an automatic computer-based model will be an effective solution. Image processing methods in conjunction with machine learning algorithms provide greater assistance in the field of plant disease detection. In the proposed work, plant leaf images of 10 crops are collected as the dataset. The images after acquisition are preprocessed using brightness preserving dynamic fuzzy histogram equalization (BPDFHE), an advanced version of histogram equalization and Gaussian filtering. The results are calculated and compared using the parameters such as peak signal to noise ratio (PSNR), structural similarity index (SSIM) and mean square error (MSE). This method performs more accurately than the existing preprocessing approaches.

show abstract

“…In [15], the authors outline the technical processes for pre-processing a plant image in order to extract its important visual features as a separate step prior to feeding the extracted features into a neural network classifier, with the aim of improving its performance when classifying a plant disease. The machine learning algorithms covered are explained with particular focus on their applications to mobile classification, including covering K-means clustering for image segmentation and mobile model conversion.…”

Section: Plant-based Mobile Application Solutionsmentioning

confidence: 99%

AgroAId: A Mobile App System for Visual Classification of Plant Species and Diseases Using Deep Learning and TensorFlow Lite

et al. 2022

View full text Add to dashboard Cite

This paper aims to assist novice gardeners in identifying plant diseases to circumvent misdiagnosing their plants and to increase general horticultural knowledge for better plant growth. In this paper, we develop a mobile plant care support system (“AgroAId”), which incorporates computer vision technology to classify a plant’s [species–disease] combination from an input plant leaf image, recognizing 39 [species-and-disease] classes. Our method comprises a comparative analysis to maximize our multi-label classification model’s performance and determine the effects of varying the convolutional neural network (CNN) architectures, transfer learning approach, and hyperparameter optimizations. We tested four lightweight, mobile-optimized CNNs – MobileNet, MobileNetV2, NasNetMobile, and EfficientNetB0 – and tested four transfer learning scenarios (percentage of frozen-vs.-retrained base layers): (1) freezing all convolutional layers; (2) freezing 80% of layers; (3) freezing 50% only; and (4) retraining all layers. A total of 32 model variations are built and assessed using standard metrics (accuracy, F1-score, confusion matrices). The most lightweight, high-accuracy model is concluded to be an EfficientNetB0 model using a fully retrained base network with optimized hyperparameters, achieving 99% accuracy and demonstrating the efficacy of the proposed approach; it is integrated into our plant care support system in a TensorFlow Lite format alongside the front-end mobile application and centralized cloud database. Finally, our system also uses the collective user classification data to generate spatiotemporal analytics about regional and seasonal disease trends, making these analytics accessible to all system users to increase awareness of global agricultural trends.

show abstract

Mobile Application for Classification of Plant Leaf Diseases Using Image Processing and Neural Networks

Cited by 8 publications

References 8 publications

Revolutionizing Coffee Farming: A Mobile App with GPS-Enabled Reporting for Rapid and Accurate On-Site Detection of Coffee Leaf Diseases Using Integrated Deep Learning

Revolutionizing Coffee Farming: A Mobile App with GPS-Enabled Reporting for Rapid and Accurate On-Site Detection of Coffee Leaf Diseases Using Integrated Deep Learning

Preprocessing of leaf images using brightness preserving dynamic fuzzy histogram equalization technique

AgroAId: A Mobile App System for Visual Classification of Plant Species and Diseases Using Deep Learning and TensorFlow Lite

Contact Info

Product

Resources

About