Leaf Pathology Detection in Potato and Pepper Bell Plant using Convolutional Neural Networks

Aldhyani, Theyazn H. H.; Alkahtani, Hasan; Eunice, R. Jennifer; Hemanth, D. Jude

doi:10.1109/icces54183.2022.9835735

Cited by 11 publications

(3 citation statements)

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“…Figure 4 displays some sample images from the dataset. Since its release, several studies have been conducted on identifying plant diseases using this dataset [51][52][53][54]. The pre-trained models were trained with 80% of the PlantVillage dataset, and 20% was used for validation and testing.…”

Section: Data Materialsmentioning

confidence: 99%

Application of Drone Surveillance for Advance Agriculture Monitoring by Android Application Using Convolution Neural Network

et al. 2023

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Plant diseases are a significant threat to global food security, impacting crop yields and economic growth. Accurate identification of plant diseases is crucial to minimize crop loses and optimize plant health. Traditionally, plant classification is performed manually, relying on the expertise of the classifier. However, recent advancements in deep learning techniques have enabled the creation of efficient crop classification systems using computer technology. In this context, this paper proposes an automatic plant identification process based on a synthetic neural network with the ability to detect images of plant leaves. The trained model EfficientNet-B3 was used to achieve a high success rate of 98.80% in identifying the corresponding combination of plant and disease. To make the system user-friendly, an Android application and website were developed, which allowed farmers and users to easily detect diseases from the leaves. In addition, the paper discusses the transfer method for studying various plant diseases, and images were captured using a drone or a smartphone camera. The ultimate goal is to create a user-friendly leaf disease product that can work with mobile and drone cameras. The proposed system provides a powerful tool for rapid and efficient plant disease identification, which can aid farmers of all levels of experience in making informed decisions about the use of chemical pesticides and optimizing plant health.

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Section: Data Materialsmentioning

confidence: 99%

Application of Drone Surveillance for Advance Agriculture Monitoring by Android Application Using Convolution Neural Network

et al. 2023

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“…Recently, artificial intelligence (AI) based machine learning (ML) and deep learning (DL) techniques have played an integral role in the agricultural field, especially in the disease detection process. Recently, ML-based techniques like k-nearest neighbor (KNN) [7], support vector machine (SVM) [8], Naïve Bayes (NB) [9], random forest (RF) [10], and artificial neural network (ANN) [11]have been introduced in several studies to recognize crop leaf diseases effectively. Despite the existing techniques performing well, there are also limitations like low accuracy, high computational complexity, and poor generalization ability.…”

Section: Introductionmentioning

confidence: 99%

GSAtt-CMNetV3: Pepper Leaf Disease Classification Using Osprey Optimization

Begum,

Syed

2024

IEEE Access

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Nowadays, the demand for pepper keeps on increasing with the increase in human population. Accurate diagnosis, flawless identification, and early detection of the lesions will improve the income of farmers. At present, deep learning (DL) based techniques assist farmers in identifying plant diseases with low cost and minimal time complexity. Hence, this study proposes a novel optimized DL model for classifying the presence and absence of pepper leaf disease using an effective feature learning process. The proposed study undergoes four major stages namely pre-processing, segmentation, feature extraction, and classification. In the pre-processing stage, initially, the input images are resized and the Improved Contrast Limited Adaptive Histogram Equalization (ICLAHE) technique is introduced to enhance the quality of the pepper leaf images. Then, the Kernelized Gravity-based Density Clustering (KGDC) technique is conquered to segment the diseased portions from the leaf images. Finally, the Gated Self-Attentive Convoluted MobileNetV3 (GSAtt-CMNetV3) technique is proposed to extract the features and classify the pepper leaf disease accurately. Moreover, a novel osprey optimization algorithm (Os-OA) is introduced to tune the parameters of the proposed DL model for enhancing the classification performance. The proposed study is implemented via the Python platform, and a publicly available Plant-Village dataset is utilized for the simulation process. Accuracy, precision and recall values achieved by the proposed pepper leaf disease classification for training percent 80 is 97.87%, 96.87% and 97.08% respectively.

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“…The projected potato leaf detection model extended by 90% and 78.20% of accuracy and regular precision, respectively. Monika Lamba et al, (2021) [30] defined an analysis of potato plant disorders as a severe portion of better knowledge of a nation's economic perspective in the form of undeveloped harvest. The preliminary detection and procedure of conditions in root plant leaves were vital as they can seriously fault the class manufacture and development.…”

Section: Introductionmentioning

confidence: 99%

Optimized Matrix Feature Analysis – Convolutional Neural Network (OMFA-CNN) Model for Potato Leaf Diseases Detection System

Rohilla,

Rai

2023

IJRITCC

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One of the most often grown crops is the potato. As a main food, potatoes are prioritised for cultivation worldwide. Because potatoes are such a rich source of vitamins and minerals, we can create a robust system for food security. However, a number of illnesses delay the growth of agriculture and harm potato output. Consequently, early disease identification can offer a better answer for effective crop production. In this research work aim is to classify and detect the potato leave (PL) diseases using OMFA-CNN deep learning model. An optimized matrix feature analysis-CNN deep learning model for PL disease detection is implemented. In the first phase, the PLs features are extracted from the potato leave images using K-means clustering image segmentation method. At the last phase, a new OMFA-CNN model are proposed using CNN to classify virus, and bacterial diseases of PLs, The PL disease dataset consists 2351 images gathered in real-time and from the Kaggle (PlantVillage) dataset. The implemented OMFA-CNN model attained 99.3 % precision and 99 % recall on potato disease detection. The implemented method is also compared with MASK RCNN,SVM and other models and attained significantly high precision and recall.

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Leaf Pathology Detection in Potato and Pepper Bell Plant using Convolutional Neural Networks

Cited by 11 publications

References 0 publications

Application of Drone Surveillance for Advance Agriculture Monitoring by Android Application Using Convolution Neural Network

Application of Drone Surveillance for Advance Agriculture Monitoring by Android Application Using Convolution Neural Network

GSAtt-CMNetV3: Pepper Leaf Disease Classification Using Osprey Optimization

Optimized Matrix Feature Analysis – Convolutional Neural Network (OMFA-CNN) Model for Potato Leaf Diseases Detection System

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