Jul 2026· International Journal of Drug Delivery Technology· Vol 16· 0 citations· 13 references
TL;DR
The clinical reliability of different deep learning models of various representational capacities has been tested while using ImageNet pre-trained parameters and the experimental results show that MobileNetV2 achieves the highest accuracy of 96.15%, outperforming deep CNN (76.92%) and medium CNN (61.54%).
Abstract
Plant varieties are essential for the survival of human beings and animals, as they act as an alternative source of food,
fiber, fodder, and other raw materials for domestic needs and industries in any society. Early identification of plant
leaf diseases is very important for keeping the health of crops intact. Crops being infected can affect the overall yield
of crops, which may be detrimental to the earnings of farmers. With the emergence of artificial intelligence
technology, it has become possible to deploy systems for quicker identification of illnesses. This work has been
carried out for the prediction of plant diseases based on visual phenotypic manifestations, such as images of leaves.
For this purpose, the dataset has been created after retrieving data from the PlantVillage dataset. The clinical
reliability of different deep learning models of various representational capacities has been tested while using
ImageNet pre-trained parameters. The experimental results show that MobileNetV2 achieves the highest accuracy of
96.15%, outperforming deep CNN (76.92%) and medium CNN (61.54%). The test accuracy and class-wise F1-scores
for the CNN are observed to be substantially very high. The generalization ability and result of DCNN and MCNN are
moderate and poor, respectively, as observed. Additionally, the proposed CNN only uses the disease-affected areas on
the leaf, thus making the result more interpretable
Understanding the DL models suggested for plant leaf disease detection and classification using the YOLO principle is the primary goal of this survey and provides a comparative and performance analysis of these models by examining their techniques, merits, demerits, datasets used, and evaluation metrics.
K. Subhashini, M. Vijayakumar· International Journal of Sci...· 0 citations
Two deep learning models, a custom CNN model and a transfer learning model based on the DenseNet-121 architecture are presented, showing that the developed transfer learning model based on DenseNet-121 had superior performance over the CNN model.
H. Jeiad, S. Samaan, Omar Janeh et al.· Automation· 0 citations
Background: In the Indian economy agriculture plays important role. Many of the crops are damaged due to diseases, therefore plant leaf disease detection at early stage is important. Tomatoes are the second most consumed vegetable in Indian households, with a rank second largest producer and consumption in world. Tomatoes are of economically important crop in India with large scale cultivation. Yet, unfavourable environmental factors tend to cause numerous diseases due to bacteria, fungi and viruses that infect different plant parts. These diseases cause the yield to be lower, leading to heavy economic losses for the farmers. For effective disease management, it is important to detect the disease in early stage using the advanced techniques for maximising the yielding of crop. Methods: In the proposed approach, tomato leaf images were first enhanced using CLAHE to improve local contrast, after that data augmentation to increase dataset diversity. Four transfer learning models based on pretrained CNN architecture: VGG16, VGG19, ResNet50 and MobileNetV2, were used as fixed feature extractors, where the pre-trained layers were fixed and newly added classification layers were trained for tomato leaf disease classification. Although transfer learning has been widely applied for tomato leaf disease classification, comparative investigations on the effect of CLAHE-enhanced images across different pre-trained architectures remain limited. This study shows performance of these four CNN architectures under identical preprocessing and training conditions to analyze the influence of CLAHE-based contrast enhancement on disease feature representation and classification performance. Result: The outcomes of experimental results shows strong classification performance on the plant village dataset for detection of tomato leaf disease, achieving best accuracies of 93.75%, 95.75%, 86.38% and 98.54% using the VGG16, VGG19, ResNet50 and MobileNetV2 models respectively.
Unknown authors· Indian Journal of Agricultur...· 0 citations
The proposed automated leaf disease detection system using image processing and deep learning techniques can detect leaf diseases effectively and efficiently, making it a useful and cost-effective solution for supporting farmers and agricultural experts in early disease diagnosis.
Shilpa T. S., K. U, Anusha Jajur J· World Journal of Advanced En...· 0 citations
This study proposes a novel deep learning approach that takes into account both the specific visual characteristics of plant diseases and potential disturbances in the microstructure, such as surface irregularities or prominent leaf veins, which may mislead the model.
J. Hoffmann, Christopher Mai, Ricardo Buettner· PLoS ONE· 0 citations
Agriculture remains at the core of human life, providing staple food and livelihood for millions worldwide. Among its different domains, food crops directly enter the human system, while cash crops are grown primarily for monetary gains. Maize, as one of the most extensively grown and consumed food crops, is of gigantic economic and nutritional value, particularly in West Africa. Unfortunately, maize plant diseases have adversely impacted farmer yields, resulting in decreased maize production. This research aims to create a system that can identify diseases in maize based on images of the leaves. Three deep convolutional neural network (DCNN) models, namely MobileNetV2, InceptionV3, and ResNet50, were selected to achieve this goal because of their prior ability. The transfer learning technique was adopted to develop new models for classifying maize disease using a hybrid maize leaf image dataset comprising 6,543 images from the University of Pretoria and Kaggle repositories. Furthermore, the dataset was split into 80% for training, 10% for validation, and 10% for testing and the three model were configured and trained. According to the evaluation results, MobileNetV2 was the best model for classifying maize leaf diseases, with a 95.29% classification accuracy. In comparison, InceptionV3 and ResNet-50 yielded accuracies of 92.18% and 74.48%, respectively. The MobileNetV2 was chosen for the dual deployment in both a web-based and a mobile application due to its exceptional performance metrics and its lightweight API. Evaluation of the deployed MobileNetV2 model on both the web and mobile applications showed that it achieved an average confidence rate of 91% on both platforms, with response times of 0.159 s and 0.163 s, and throughputs of 5.88 images/s and 6.28 images/s, respectively. This research offers a simple and intuitive tool for users and agricultural professionals to quickly detect maize leaf diseases and take necessary precautions to mitigate losses.
Kennedy O. Okokpujie, Osondu C. Ronald, Joshua S. Mommoh et al.· International Journal of Eng...· 0 citations
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