Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques

Chowdhury, Muhammad E. H.; Rahman, Tawsifur; Khandakar, Amith; Ayari, Mohamed Arselene; Khan, Aftab Ullah; Khan, Muhammad Salman; Al‐Emadi, Nasser; Reaz, Mamun Bin Ibne; Islam, Mohammad Tariqul; Ali, Sawal Hamid Md

doi:10.3390/agriengineering3020020

Cited by 147 publications

(36 citation statements)

References 51 publications

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“…[188]. Improvement of accuracy in the present Computer Vision plant phenotyping methods using Deep Learning [189][190][191][192], Fuzzy [193] logic, etc. Biomass and quantity mapping [194], dead or diseased tree detection [195] using Unmanned Aerial Vehicle (UAV) or drone-based imaging.…”

Section: Discussion and Future Research Scopementioning

confidence: 99%

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Hati

Singh

2021

AgriEngineering

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Conventional farming necessitates a large number of resources and infrastructure such as land, irrigation, manpower to manage farms, etc. Modern initiatives are required to automate conventional farms. Smart indoor farms offer the potential to remedy the shortfalls of conventional farms by providing a controlled, intelligent, and smart environment. This paper presents a three-dimensional perspective consisting of soilless farming, energy harvesting, and smart technologies, which could be considered as the three important characteristics of smart indoor farms. A six-layer smart indoor farms architecture has also been proposed, which explains how data are collected using various sensors and devices and then transmitted onto the cloud infrastructure for further analysis and control through various layers. Artificial lighting, smart nutrition management, and artificial climate control, to name a few, are some of the important requirements for smart indoor farms while considering control and service management factors. The major bottleneck in installing such systems is both the economical and the technical constraints. However, with the evolution of technology (and when they become widely available in the near future), a more favourable farming scenario may emerge. Furthermore, smart indoor farms could be viewed as a potential answer for meeting the demands of a sustainable agricultural revolution as we move closer to Agriculture 4.0. Finally, in order to adapt smart indoor farms and their study scope, our work has presented various research areas to potential researchers.

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Section: Discussion and Future Research Scopementioning

confidence: 99%

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Hati

Singh

2021

AgriEngineering

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“…These use highly accurate methods for identifying plant disease in tomato leaves. In addition, researchers have proposed many deep learning-based solutions in disease detection and classification, as discussed below in [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ].…”

Section: Related Workmentioning

confidence: 99%

“…The improved U-net segmentation model correctly classified 98.66% of leaf pictures for segmentation. EfficientNet-B7 surpassed 99.95% and 99.12% accuracy for binary and six-class classification, and EfficientNet-B4 classified images for ten classes with 99.89 percent accuracy [ 47 ].…”

Section: Related Workmentioning

confidence: 99%

Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network

Trivedi

Gautam

Anand

et al. 2021

Sensors

182

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Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease’s effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate.

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“…To handle the unbalanced datasets, data augmentation techniques like rotation, scaling, and translation are applied. The proposed modified U‐Net model using augmented images has achieved an overall accuracy of 98.66% (Chowdhury et al, 2021). A new augmentation technique CycleGAN is proposed to accommodate the augmentation process.…”

Section: Related Workmentioning

confidence: 99%

Convolution network model based leaf disease detection using augmentation techniques

et al. 2021

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Agriculture plays a vital role in a country's economy. Thus, better yields of crops are required for growth of agriculture‐based industries. Deep Learning (DL) techniques have led to remarkable achievements in image classification and recognition. However, DL networks rely heavily on large data sets to prevent overfitting. Image augmentation is one of the DL techniques gaining attention in avoiding the risk of overfitting. The most common Image augmentation techniques like rotation, zoom, and shift used in the existing research allow to generate new images from the original set and increases the images quantity but cannot minimize the misclassification error. The present research can provide a better solution to provide sufficient quantity of training images to a convolutional neural network model to handle the overfitting and classification problems. Therefore, two learning algorithms image preprocessing and transformation algorithm (IPTA) and image masking and REC‐based hybrid segmentation algorithm (IMHSA) are proposed to address the problem of limited dataset and convolutional neural network model overfitting during classification. IPTA is an adaptive supervised learning approach to transform the original images into augmented ones and IMHSA is an unsupervised approach for Red, Green, Blue (RGB) image segmentation. Later, the Histogram threshold technique is applied to form all the possible regions used to split the diseased leaf into comparable regions. A novel convolutional neural network model is also proposed to evaluate the performance of the IPTA approach. The model is trained on two independent datasets, one generated before and one generated after IPTA was applied. Plots of precision and loss functions are used to assess the acquired results. The experimental results demonstrated that before using IPTA, the training accuracy was 83%, while the validation accuracy was 65%. After using IPTA, the proposed model attained a training accuracy of 74% and a validation accuracy of 73%, thereby solving the overfitting problem. The experimental results proved that the proposed model outperforms while classifying the RGB images with the support of image augmentation.

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Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques

Cited by 147 publications

References 51 publications

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network

Convolution network model based leaf disease detection using augmentation techniques

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