A Polyhouse: Plant Monitoring and Diseases Detection using CNN

Radha, N.; Swathika, R.

doi:10.1109/icais50930.2021.9395847

Cited by 20 publications

(4 citation statements)

References 9 publications

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“…Despite the widespread use of CNN, we have observed that only a very limited amount of research has been done on the effects of choosing different hyperparameters as well as how to improve CNN performance. The same previously mentioned result has also been published and supported in some other pertinent papers that have employed the same deep-learning techniques to identify potato leaf disease in the agricultural field (Lee et al, 2021;Radha & Swathika, 2021). Since this work has been extensively examined in this article, we can happily state that the methods used to configure the hyperparameters in deep learning techniques where CNN is used to model the detection of potato leaf disease seem to be still lacking on a larger scale in the agricultural sector.…”

Section: Deep Learning Techniques For Potato Leaf Disease Detectionsupporting

confidence: 68%

Deep learning hyperparameter’s impact on potato disease detection

Asfaw

2023

The Scientific Temper

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In this study, we reviewed various published works that used deep learning techniques to detect potato leaf disease. Deep learning techniques have shown remarkable detection performance for potato leaf disease. In particular, CNN has been shown to be efficient in extracting features from images and in identifying patterns that are challenging to identify using machine learning techniques. However, CNN architectures with different activation functions, batch sizes, and optimizers can cause different results. Therefore, in this work, a CNN model has been implemented to analyze the effect of different activation functions, batch sizes, and optimizers for the detection of potato leaf diseases. Based on the findings of three experiments, the leaky rectifier function performed best as the activation function for the convolutional neural network (CNN) model. AdaGrad’s optimizer showed superior accuracy compared to stochastic gradient descent (SGD), Adam, Adamax, and RMSProp algorithms. We also discovered that the model’s performance was even better, but only when the batch size used in the model was smaller than the size of the test dataset. The work is based on deep learning to identify potato leaf disease and provide researchers and practitioners with heuristic knowledge to help increase potato production when CNN is employed in the agricultural sector.

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Section: Deep Learning Techniques For Potato Leaf Disease Detectionsupporting

confidence: 68%

Deep learning hyperparameter’s impact on potato disease detection

Asfaw

2023

The Scientific Temper

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“…The ideal humidity for vegetable plants cultivation is 50% to 60% [39]. Soil moisture sensors placed inside the root of plants analyze soil moisture level values to facilitate optimum utilization of water resources [40,41].…”

Section: Soil Monitoringmentioning

confidence: 99%

Everything You wanted to Know about Smart Agriculture

Mitra¹,

Vangipuram²,

Bapatla³

et al. 2022

Preprint

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The world population is anticipated to increase by close to 2 billion by 2050 causing a rapid escalation of food demand. A recent projection shows that the world is lagging behind accomplishing the "Zero Hunger" goal, in spite of some advancements. Socio-economic and well being fallout will affect the food security. Vulnerable groups of people will suffer malnutrition. To cater to the needs of the increasing population, the agricultural industry needs to be modernized, become smart, and automated. Traditional agriculture can be remade to efficient, sustainable, eco-friendly smart agriculture by adopting existing technologies. In this survey paper the authors present the applications, technological trends, available datasets, networking options, and challenges in smart agriculture. How Agro Cyber Physical Systems are built upon the Internet-of-Agro-Things is discussed through various application fields. Agriculture 4.0 is also discussed as a whole. We focus on the technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) which support the automation, along with the Distributed Ledger Technology (DLT) which provides data integrity and security. After an in-depth study of different architectures, we also present a smart agriculture framework which relies on the location of data processing. We have divided open research problems of smart agriculture as future research work in two groups -from a technological perspective and from a networking perspective. AI, ML, the blockchain as a DLT, and Physical Unclonable Functions (PUF) based hardware security fall under the technology group, whereas any network related attacks, fake data injection and similar threats fall under the network research problem group.

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“…The results indicated that the projected algorithm provided the accuracy around 96.50%. A Convolutional Neural Network (CNN) model was investigated by Akshay Kumar, et.al (2019) for classifying the image so that the plant disease was detected [24]. The experiments were conducted on the investigated model in order to detect diseases in tomato leaves.…”

Section: Literature Reviewmentioning

confidence: 99%

survey of plant disease detection techniques based on image processing and machine learning

Kumar

Jindal

2022

ijhs

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The Plant disease identification is the key which prevent loss and improve quality of the agriculture products. The plant disease detection techniques have various phases which include pre-processing, segmentation, feature extraction and classification. In the pre-processing stage, the contrast of the input image is increased. The points which are not visible are highlighted. The second stage is the segmentation which can help in selecting the region of interest. The feature extraction phase will extract relevant features of the plant. In the last phase, classification algorithm will be applied which can predict name of the disease. The various schemes are already being proposed for the plant disease detection. In this paper, plant disease detection schemes are reviewed in terms of methodologies and outcomes.

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A Polyhouse: Plant Monitoring and Diseases Detection using CNN

Cited by 20 publications

References 9 publications

Deep learning hyperparameter’s impact on potato disease detection

Deep learning hyperparameter’s impact on potato disease detection

Everything You wanted to Know about Smart Agriculture

survey of plant disease detection techniques based on image processing and machine learning

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