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All Journal International Journal of Informatics and Communication Technology (IJ-ICT)
Chaudhari, Nandini
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Computer Science & IT

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Deep learning for grape leaf disease detection Patil, Pragati; Jadhav, Priyanka; Chaudhari, Nandini; Sureja, Nitesh; Pawar, Umesh
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp653-662

Abstract

Agriculture is crucial to India's economy. Agriculture supports almost 75% of the world's population and much of its gross domestic product (GDP). Climate and environmental changes pose a threat to agriculture. India is recognized for its grapes, a commercially important fruit. Diseases reduce grape yields by 10-30%. If not recognized and treated early, grape diseases can cost farmers a lot. The main grape diseases include downy and powdery mildew, leaf blight, esca, and black rot. This work creates an Android grape disease detection app which uses machine learning. When a farmer submits a snapshot of a diseased grape leaf, the smartphone app identifies the ailment and offers grape plant disease prevention tips. In this research, an android app that detects grape plant illnesses use convolutional neural network (CNN) and AlexNet machine learning architectures. We investigated and compared CNN and AlexNet architecture's efficacy for grape disease detection using accuracy and other metrics. The dataset used comes from Kaggle. CNN and AlexNet architectures yielded 98.04% and 99.03% accuracy. AlexNet was more accurate than CNN in the final result.
An artificial intelligent system for cotton leaf disease detection Jadhav, Priyanka Nilesh; Patil, Pragati Prashant; Sureja, Nitesh; Chaudhari, Nandini; Sureja, Heli
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp950-959

Abstract

This study aims to develop a deep learning-based system for the detection and classification of diseases in cotton leaves, with the goal of aiding in early diagnosis and disease management, thereby enhancing agricultural productivity in India. The study utilizes a dataset of cotton leaf images, classified into four categories: Fusarium wilt, Curl virus, Bacterial blight, and Healthy leaves. The dataset is used to train and evaluate various CNN models such as basic CNN, VGG19, Xception, InceptionV3, and ResNet50. These models were evaluated on their accuracy in identifying the presence of diseases and classifying cotton leaf images into the respective categories. The models were trained using standard deep learning frameworks and optimized for high performance. The results indicated that ResNet50 achieved the highest accuracy of 100%, followed by InceptionV3 with 98.75%, and VGG19 and Xception both with 97.50%. The basic CNN model showed an accuracy of 96.25%. These models demonstrated strong potential for accurate multi-class classification of cotton leaf diseases. This study emphasizes the potential of deep learning in agricultural diagnostics. Future research can focus on improving model robustness, incorporating larger datasets, and deploying the system for real-time field use to assist farmers in disease management and improving cotton production.
Co-Authors Jadhav, Priyanka Jadhav, Priyanka Nilesh Patil, Pragati Patil, Pragati Prashant Pawar, Umesh Sureja, Heli Sureja, Nitesh