Plant Diseases Concept in Smart Agriculture Using Deep Learning

Chauhan, Prachi; Mandoria, Hardwari Lal; Negi, Atul; Rajput, Ravindra Singh

doi:10.4018/978-1-7998-5003-8.ch008

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…The performance of existing deep learning models like LeNet [35], AlexNet [26], CaffeNet [36], VGG [25], GoogleNet [27], Inception [37], Inception-ResNet [38], and MobileNet [39,40] were compared when applied to the ImageNet dataset. Each CNN differs from the other based on the layer count and structure.…”

Section: -1-relevant Studiesmentioning

confidence: 99%

Smart Farm-Care using a Deep Learning Model on Mobile Phones

Francis¹,

Anbananthen

Deisy³

et al. 2023

Emerg Sci J

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Deep learning and its models have provided exciting solutions in various image processing applications like image segmentation, classification, labeling, etc., which paved the way to apply these models in agriculture to identify diseases in agricultural plants. The most visible symptoms of the disease initially appear on the leaves. To identify diseases found in leaf images, an accurate classification system with less size and complexity is developed using smartphones. A labeled dataset consisting of 3171 apple leaf images belonging to 4 different classes of diseases, including the healthy ones, is used for classification. In this work, four variants of MobileNet models - pre-trained on the ImageNet database, are retrained to diagnose diseases. The model’s variants differ based on their depth and resolution multiplier. The results show that the proposed model with 0.5 depth and 224 resolution performs well - achieving an accuracy of 99.6%. Later, the K-means algorithm is used to extract additional features, which helps improve the accuracy to 99.7% and also measures the number of pixels forming diseased spots, which helps in severity prediction. Doi: 10.28991/ESJ-2023-07-02-013 Full Text: PDF

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Section: -1-relevant Studiesmentioning

confidence: 99%

Smart Farm-Care using a Deep Learning Model on Mobile Phones

Francis¹,

Anbananthen

Deisy³

et al. 2023

Emerg Sci J

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“…The author in [4] has implemented the CNN model for classification and detection of plant leaf disease among 10 different classes. In [5], the author has proposed a lightweight Deep Neural Networks (DNN) which can run on resource constrained IOT device. Different image sizes have been tested with this architecture to find the optimal size of the input image.…”

Section: Literature Reviewmentioning

confidence: 99%

IOT and Deep Learning Assisted Smart Agriculture System for Plant Disease Identification

Selvaraj

Thangarajan²,

Santhosh

et al. 2023

ACE

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Agriculture employed more than 50% of labours and contributed nearly 18% to country's GDP. India is the leading producers of Many crops, such as wheat, rice, lentils, cotton, peanuts and perishable products but in recent years, the percentage of people getting into agriculture has been reduced. Crop disease and incorrect maintenance of humidity and moisture is the major reason that makes new farmers frustrated. Developing a deep learning model, that can identify the crop disease with high accuracy, and using this model one can easily identify the crop disease which makes easier for them treat the crop with pesticide, so that we cant wait for the experts. Using the IOT architecture to find the humidity and soil moisture of the field so that the farmer can maintain the correct level of humidity and soil moisture through irrigation, sometimes extreme high and very low moisture and humidity leads to diseases. An enhanced classification model was suggested in this paper to classify leaf diseases. Dataset with more than 10000 images is used for image classification. Classification accuracy is achieved using AlexNet model and it came out to be the highest in comparison to other models, and IOT part using the Raspberry pi 3 kit with soil moisture and humidity and soil moisture sensor.

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“…Deep Learning, Data Fusion [51] Develops a deep learning spatiotemporal strategy for data fusion in agricultural surveillance using remote sensing. Deep Learning [52] Introduces a deep learning method for leaf disease detection in various plants, enhancing accuracy and early diagnosis. Backpropagation Algorithm, LSTM [53] Compares Backpropagation and LSTM algorithms for plant environmental condition prediction in smart agriculture.…”

Section: Techniquementioning

confidence: 99%

Review of Sustainable Irrigation Technological Practices in Agriculture

Baskar M

2023

IJRITCC

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The paper focuses on the increasing demand for water and its impact on irrigated agriculture, emphasizing the importance of effective water management. It reviews the use of soil moisture sensors, IoT, big data analytics, and machine learning in agriculture, particularly in the context of Indian agriculture. The study explores the potential of IoT technologies, such as sensors, drones, and machine learning algorithms, to optimize water usage, minimize waste, and enhance crop yields. The role of big data analytics in sustainable water irrigation management and decision support systems is highlighted. The integration of IoT and sensory systems in smart agriculture is discussed, addressing both the challenges and benefits of implementing sensory-based irrigation systems. Additionally, the paper describes an automated irrigation system developed to optimize water use for crops, utilizing a distributed wireless network of sensors and a web application. The system, powered by photovoltaic panels, demonstrated significant water savings of up to 90% compared to traditional irrigation methods in a sage crop field. The system's energy autonomy and cost-effectiveness suggest its potential utility in water-limited and geographically isolated areas.

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Plant Diseases Concept in Smart Agriculture Using Deep Learning

Cited by 8 publications

References 18 publications

Smart Farm-Care using a Deep Learning Model on Mobile Phones

Smart Farm-Care using a Deep Learning Model on Mobile Phones

IOT and Deep Learning Assisted Smart Agriculture System for Plant Disease Identification

Review of Sustainable Irrigation Technological Practices in Agriculture

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