Image-Based Crop Leaf Disease Identification Using Convolution Encoder Networks

Bharathi, Indira; Sonai, Veeramani

doi:10.5772/intechopen.106989

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…In 2022, Bharathiet al, [3] haveexecuted a hybrid method by a combination of CNN and an autoencoder for detecting crop leaf diseases.Initially, using CNN with image processing methodologies such as image segmentation and filtering the disease in the crop was detected. The ML algorithms better recognize the plants, weed discrimination, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

“…One such challenge is accuracyand quality in result quality then the process needs higherepochs. In [3][ important role in these methods. This helps in the early detection of diseases.…”

Section: Challengesmentioning

confidence: 99%

“…To achieve higher quality then the process needs higherepochs. In [3][16] Farmer's experience of past dynastieshas an important role in these methods. This helps in the early detection of diseases.…”

Section: 11image Cuttingmentioning

confidence: 99%

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An Improved Crop Disease Identification Based on the Convolutional Neural Network

Abdelouafi Boukhris

2023

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The increase in population leads to an increase in the need for food production. A healthy, pest-free plant can providea considered amount of yield in time. However, many conditions affect crop production.Identifying crop disease accurately, fast, and intelligently, plays an important role in agriculture informatization development. Most existing methods are performed manually, which affects the identifyingresult. Careful monitoring and diagnosis of crops for a large area manually is a tedious process. To address these issues, we proposed an improved crop disease identification based on the convolutional neural network (CNN) architecture.The first operation is to resize crop images and to be normalizedthem.Here, we built a neural network toload data samples for training and dividedthe verification set and training set. Furthermore, to adjust the learning rate dynamically, we use Adam algorithms which combinedthe RMSprop algorithm and momentum algorithm to improve the training learning rate.Finally, we used PlantVillage dataset to carry out the validations, this dataset contains 38 different types of crops. The experimentalresult showed the test accuracy and validation accuracy are100% and 97.50% respectively. Compared with state-of-the-art methods, our proposed model has higher detection accuracy. The convolutional neural network proposed in this paper has a high accuracy and fast training speed. The proposed architecture is less time-consuming which can help to improve the development of smart agriculture.

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Section: Literature Reviewmentioning

confidence: 99%

“…One such challenge is accuracyand quality in result quality then the process needs higherepochs. In [3][ important role in these methods. This helps in the early detection of diseases.…”

Section: Challengesmentioning

confidence: 99%

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An Improved Crop Disease Identification Based on the Convolutional Neural Network

Abdelouafi Boukhris

2023

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“…Many research works presented varied state-of-the-art systems for crop diseases identification using machine learning and deep learning algorithms. Nandhini and Ashokkumar [1] uses DenseNet-121 for plant leaf disease identification, Shadin et al [2] use convolutional neural network and Inception V3 COVID-19 diagnosis from chest X-ray images, Khan et al [3] use SqueezeNet to classify diseases in citrus fruits and the accuracy was 96%, Bharathi and Sonai [4] uses convolution encoder method to detect leaf disease and the accuracy was 98%.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning and Machine Learning Based Method for Crop Disease Detection and Identification Using Autoencoder and Neural Network

Boukhris,

Jilali,

Asri

2024

RIA

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Crop diseases present a major threat to agricultural output, disrupting both the quantity and quality of production. Disease diagnosis remains a challenge for farmers, primarily due to limited knowledge and the need for specialized agricultural engineering expertise. To solve these problems, a new technique named the Autoencoder Latent Space-Neural Network (ALS-NN) was introduced in this study. It combines the strengths of autoencoders and neural networks to find crop diseases. Data processing is the first step of the methodology, and then data compression into a latent space follows. This compressed data serves as the input for the neural network, facilitating efficient crop disease classification. This approach capitalizes on the autoencoder's capacity for dimensionality reduction, data compression, and encoding, which is particularly beneficial when handling high-dimensional data. The reduced data dimensionality enables the neural network to process the information more efficiently. The ALS-NN model, by compressing data, focuses on the crucial information for the classification process, thereby enhancing computational speed and reducing the number of trained parameters. This results in time efficiencies during disease detection operations, mitigating the detrimental effects of diseases on crop yields. The integration of autoencoders and neural networks forms a potent strategy for disease detection, leveraging the autoencoders' capabilities for dimensionality reduction, anomaly detection, and feature learning, coupled with the classification and generalization abilities of neural networks. This hybrid approach can potentially lead to more precise, efficient, and interpretable disease detection system. PlantVillage is used with 10 crop types. We used the first part of autoencoder (The encoder) to compress images into Latent space; for classification, the result is subsequently fed into a neural network. Our model (ALS-NN) achieved 90% for test accuracy and 90% for validation accuracy.

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Metaheuristics Feature Selection Algorithms for Identification and Classification of Mango Pests Diseases

Uchimuthu,

Sonai,

Chitra

et al. 2023

Signals and Communication Technology

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Image-Based Crop Leaf Disease Identification Using Convolution Encoder Networks

Cited by 5 publications

References 23 publications

An Improved Crop Disease Identification Based on the Convolutional Neural Network

An Improved Crop Disease Identification Based on the Convolutional Neural Network

Deep Learning and Machine Learning Based Method for Crop Disease Detection and Identification Using Autoencoder and Neural Network

Metaheuristics Feature Selection Algorithms for Identification and Classification of Mango Pests Diseases

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