Towards a Multimodal System for Precision Agriculture using IoT and Machine Learning

Garg, Satvik; Pundit, Pradyumn; Jindal, Himanshu; Saini, Hemraj; Garg, Somya

doi:10.1109/icccnt51525.2021.9579646

Cited by 28 publications

(13 citation statements)

References 15 publications

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“…Garg et al [ 9 ] implemented a multimodal system for precision agriculture using Internet of Things (IoT) and machine learning techniques; the way of increasing the crop production in the entire networks of agricultural farming is to reduce the entire plant disease by the use of the variety of techniques and the algorithm and, then it produces the enhanced quantity and quality of the things supplied in the form of the enhanced way. Finally, their paper implements the enhanced accuracy when compared with the existing techniques.…”

Section: Review Of Literaturementioning

confidence: 99%

Deep Transfer Learning Technique for Multimodal Disease Classification in Plant Images

Balaji

Anushkannan²,

Sujatha³

et al. 2023

Contrast Media & Molecular Imaging

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Rice (Oryza sativa) is India’s major crop. India has the most land dedicated to rice agriculture, which includes both brown and white rice. Rice cultivation creates jobs and contributes significantly to the stability of the gross domestic product (GDP). Recognizing infection or disease using plant images is a hot study topic in agriculture and the modern computer era. This study paper provides an overview of numerous methodologies and analyses key characteristics of various classifiers and strategies used to detect rice illnesses. Papers from the last decade are thoroughly examined, covering studies on several rice plant diseases, and a survey based on essential aspects is presented. The survey aims to differentiate between approaches based on the classifier utilized. The survey provides information on the many strategies used to identify rice plant disease. Furthermore, model for detecting rice disease using enhanced convolutional neural network (CNN) is proposed. Deep neural networks have had a lot of success with picture categorization challenges. We show how deep neural networks may be utilized for plant disease recognition in the context of image classification in this research. Finally, this paper compares the existing approaches based on their accuracy.

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Section: Review Of Literaturementioning

confidence: 99%

Deep Transfer Learning Technique for Multimodal Disease Classification in Plant Images

Balaji

Anushkannan²,

Sujatha³

et al. 2023

Contrast Media & Molecular Imaging

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“…Ponnusamy Vijayakumar and Natarajan, Sowmya [21] explore the potential of IoT, augmented reality, and machine learning in agriculture. Garg Satvik et al [22] propose IoTbased soil moisture and nutrients monitoring for irrigation water and fertilizer recommendation with a pre-trained Convolution Neural Network (CNN). Sirisha and Sahitya [23] propose ET prediction with IoT-assisted soil moisture monitoring with help of Kernel Canonical Correlation Analysis (KCCA) by using the Support Vector Machine (SVM) with kernel function for smart irrigation water scheduling.…”

Section: Literature Reviewmentioning

confidence: 99%

Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation

et al. 2022

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Accurate Evapotranspiration for saline soils (ETs) is important as well as challenging for the reclamation of saline soils through an effective leaching process. Evapotranspiration (ET) by FAO-56 Penman-Monteith standard method is complex, especially for saline soils. Moreover, existing studies focus on the use of the Internet of Things (IoT) and machine learning-enabled smart and precision irrigation water recommendation systems along with the ET estimation by limited parameters. The ETs for saline soils are also equally important for the reclamation of saline soils, which is ignored by the existing literature. The study proposed IoT and machine leaching-based architecture of context-aware monthly ETs estimations for saline soil reclamation with the effective leaching process. The IoT-enabled crop field contexts in terms of crop field temperature, soil salinity, and irrigation water salinity are used as input features to the Long Short-Term Memory (LSTM) and ensembled LSTM models for monthly ETs predictions. The performance of the proposed solution is observed in terms of the accuracy of the machine learning models along with the comparison against the FAO-56 PM-based standard method. The implementation of the proposed solution reveals that the ensembled LSTM-based approach for ETs is more accurate as compared to the LSTM model with accuracies of 92 and 90% for the training and validation datasets, respectively. The predictions made by the ensembled LSTM are more in line with the FAO-56 PM-based method with a Pearson correlation of 0.916 as compared to LSTM models. The implementation of the proposed solution in real-time environments reveals that the proposed solution is more effective in reducing the soil salinity as compared to the traditional method.

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“…Nevertheless, its correct functioning requires complicated calibration, taking factors, namely water salinity, soil structures and texture, temperature, and the spatial inconsistency of the soil. Other sensors, such as multispectral and thermal cameras, infrared radiometers (IR), or satellites, were utilized for estimating water crop requirements [6].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Sensor Enabled Irrigation Management for Intelligent Agriculture Using Hybrid Deep Belief Network

Yonbawi¹,

Alahmari²,

Raju³

et al. 2023

Computer Systems Science and Engineering

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Artificial intelligence (AI) technologies and sensors have recently received significant interest in intellectual agriculture. Accelerating the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart agriculture. Automatic irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation techniques. Automatic learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an agronomist. With this motivation, this study develops a modified black widow optimization with a deep belief network-based smart irrigation system (MBWODBN-SIS) for intelligent agriculture. The MBWODBN-SIS algorithm primarily enables the Internet of Things (IoT) based sensors to collect data forwarded to the cloud server for examination purposes. Besides, the MBWODBN-SIS technique applies the deep belief network (DBN) model for different types of irrigation classification: average, high needed, highly not needed, and not needed. The MBWO algorithm is used for the hyperparameter tuning process. A wideranging experiment was conducted, and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

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Towards a Multimodal System for Precision Agriculture using IoT and Machine Learning

Cited by 28 publications

References 15 publications

Deep Transfer Learning Technique for Multimodal Disease Classification in Plant Images

Deep Transfer Learning Technique for Multimodal Disease Classification in Plant Images

Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation

Modeling of Sensor Enabled Irrigation Management for Intelligent Agriculture Using Hybrid Deep Belief Network

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