A novel smartphone application for early detection of habanero disease

Babatunde, Ronke Seyi; Babatunde, Akinbowale Nathaniel; Ogundokun, Roseline Oluwaseun; Yusuf, Obiwusi Kolawole; Sadiku, Peter O.; Shah, Mohd Asif

doi:10.1038/s41598-024-52038-y

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…The model's performance may be affected by the limited dataset and the risk of overfitting. Moreover, the application lacked features to support farmers, and the integration of the model into the application was not assessed [32].…”

Section: Related Workmentioning

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

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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.

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Section: Related Workmentioning

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

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PND-Net: plant nutrition deficiency and disease classification using graph convolutional network

Bera,

Bhattacharjee,

Krejcar

2024

Sci Rep

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Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. Hence, continuous health monitoring of plant is very crucial for handling plant stress. The deep learning methods have proven its superior performances in the automated detection of plant diseases and nutrition deficiencies from visual symptoms in leaves. This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN), added upon a base convolutional neural network (CNN). Sometimes, a global feature descriptor might fail to capture the vital region of a diseased leaf, which causes inaccurate classification of disease. To address this issue, regional feature learning is crucial for a holistic feature aggregation. In this work, region-based feature summarization at multi-scales is explored using spatial pyramidal pooling for discriminative feature representation. Furthermore, a GCN is developed to capacitate learning of finer details for classifying plant diseases and insufficiency of nutrients. The proposed method, called Plant Nutrition Deficiency and Disease Network (PND-Net), has been evaluated on two public datasets for nutrition deficiency, and two for disease classification using four backbone CNNs. The best classification performances of the proposed PND-Net are as follows: (a) 90.00% Banana and 90.54% Coffee nutrition deficiency; and (b) 96.18% Potato diseases and 84.30% on PlantDoc datasets using Xception backbone. Furthermore, additional experiments have been carried out for generalization, and the proposed method has achieved state-of-the-art performances on two public datasets, namely the Breast Cancer Histopathology Image Classification (BreakHis 40$$\times $$ × : 95.50%, and BreakHis 100$$\times $$ × : 96.79% accuracy) and Single cells in Pap smear images for cervical cancer classification (SIPaKMeD: 99.18% accuracy). Also, the proposed method has been evaluated using five-fold cross validation and achieved improved performances on these datasets. Clearly, the proposed PND-Net effectively boosts the performances of automated health analysis of various plants in real and intricate field environments, implying PND-Net’s aptness for agricultural growth as well as human cancer classification.

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Development of disease detection mobile application for pineapple

Sophan,

Ismail,

Sadikan

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Pineapple (Ananas comosus) is a tropical fruit that is widely distributed throughout the world. The cultivation of pineapples, which holds great economic importance in the field of agriculture, encounters ongoing difficulties regarding the detection and control of diseases that affect both the quantity and quality of the yield. Manual disease detection in pineapple crops is challenging due to subjective evaluations, laborious processes, and financial losses. Manual methods can lead to delayed disease identification and specialised knowledge, limiting precise disease diagnosis. The need for effective disease detection tools has prompted the investigation of mobile applications as an ideal solution. To address these issues, automated disease scanning technology systems utilising computer vision and machine learning are essential. The Successive Approximation Model (SAM) was used for instructional design that emphasises a rapid and iterative development process. The development process involves three phases: design, development, and evaluation. The developed mobile application was then evaluated using a questionnaire to get user’s feedback and evaluate the accuracy of information provided by the application. Reporting on the result from Likert Scale, most of the score showed values four and above which indicated positive response on the usability and accuracy of disease detection by the mobile application. As a conclusion, the development of this mobile application gives a positive response to users to promptly detect and control diseases, thereby reducing the possibility of crop damage.

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A novel smartphone application for early detection of habanero disease

Cited by 4 publications

References 33 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

PND-Net: plant nutrition deficiency and disease classification using graph convolutional network

Development of disease detection mobile application for pineapple

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