Classification and yield prediction in smart agriculture system using IoT

Gupta, Akanksha; Nahar, Priyank

doi:10.1007/s12652-021-03685-w

Cited by 49 publications

(13 citation statements)

References 29 publications

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“…Around 85% water of in these countries is used in irrigation and a huge amount of data is lost in irrigation due to inefficient and unreliable techniques. Different IoT-based solutions are provided by authors in [21][22][23], aiming towards assisting farmers in fulfilling the gap between demand and supply of agricultural yields while guaranteeing reasonable profit and environmental preservation. The authors in [21] state that the agricultural sector is evolving day by day and becoming more datacentric and precise.…”

Section: Employ Principal Component Analysis To Extractmentioning

confidence: 99%

“…Different IoT-based solutions are provided by authors in [21][22][23], aiming towards assisting farmers in fulfilling the gap between demand and supply of agricultural yields while guaranteeing reasonable profit and environmental preservation. The authors in [21] state that the agricultural sector is evolving day by day and becoming more datacentric and precise. Therefore, advanced technologies like IoT networks, ML, and artificial intelligence-based solutions are being proposed for smart agriculture to enhance crops' yield and profitability while simultaneously reducing the amount of irrigation waste.…”

Section: Employ Principal Component Analysis To Extractmentioning

confidence: 99%

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Internet of Things (IoT)-Enabled Machine Learning Models for Efficient Monitoring of Smart Agriculture

Aldossary,

Alharbi,

Anwar Ul Hassan

2024

IEEE Access

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Advancements in the agricultural sector are essential because the need for food is rising as the population of the world is expanding day by day. Traditional agricultural practices are not able to fulfill these needs. Furthermore, these practices are manual and are not optimized resulting in the wastage of resources, which is not suitable for resource-constrained agricultural environments. Besides this, the Internet of Things (IoT) network is playing an important role in the modern farming system. In this paper, we introduce an innovative IoT-enabled hybrid model for smart agriculture, integrating Machine Learning (ML) and Artificial Intelligence (AI) algorithms to provide a cost-effective and reliable decision-making system. Furthermore, we introduce a robust anomaly detection mechanism while applying the capabilities of Multilayer Perceptron (MLP), Naïve Bayes, and Support Vector Machine (SVM) on the dry beans' dataset. Hybrid models, combining neural networks with Random Forest and SVM, were also explored for anomaly detection in the dataset. Furthermore, deep learning models known as MobileNetV2, VGG16, and InceptionV3 are used for the classification of soil type datasets. The hybrid deep learning models were also developed, incorporating InceptionV3 with Long Short-Term Memory (LSTM) and VGG16 with fully connected dense layers. Two types of data sets are used in this study, which are the dry beans dataset (2021) and soil type Dataset (2024). Both datasets contain images. The ML techniques are applied to these datasets for anomaly detection. The simulations results show that the classification performance of the MobileNetV2 model, it has an accuracy and recall of 0.97. It shows that the model can correctly identify the soil type around 97%. On the other hand, the hybrid model combining random forest and neural network achieved an accuracy of 92%, further validating the effectiveness of our approach. Furthermore, the SVM model achieves an impressive overall accuracy of 0.93. Additionally, this accuracy is further enhanced with the integration of SVM and neural networks. Similarly, the hybrid model combining inception V3 with the LSTM layer exhibits a notable accuracy of 0.91, highlighting its efficiency in accurately classifying various instances. Lastly, the hybrid model employing random forest and neural network architecture achieves a commendable accuracy of 92%.

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Section: Employ Principal Component Analysis To Extractmentioning

confidence: 99%

Section: Employ Principal Component Analysis To Extractmentioning

confidence: 99%

Internet of Things (IoT)-Enabled Machine Learning Models for Efficient Monitoring of Smart Agriculture

Aldossary,

Alharbi,

Anwar Ul Hassan

2024

IEEE Access

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“…Additionally, these elements are connected within various layers of IoT architectures for SA applications. However, determining an architecture for -based SA poses challenges due to the extensive potential scale and specific requirements, such as soil conditions, weather dynamics, and geographical variations [13], [42], [43].…”

Section: A Iot Architecturesmentioning

confidence: 99%

Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey

Mowla,

Shah

et al. 2023

IEEE Access

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The increasing food scarcity necessitates sustainable agriculture achieved through automation to meet the growing demand. Integrating the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) is crucial in enhancing food production across various agricultural domains, encompassing irrigation, soil moisture monitoring, fertilizer optimization and control, early-stage pest and crop disease management, and energy conservation. Wireless application protocols such as ZigBee, WiFi, SigFox, and LoRaWAN are commonly employed to collect real-time data for monitoring purposes. Embracing advanced technology is imperative to ensure efficient annual production. Therefore, this study emphasizes a comprehensive, futureoriented approach, delving into IoT-WSNs, wireless network protocols, and their applications in agriculture since 2019. It thoroughly discusses the overview of IoT and WSNs, encompassing their architectures and summarization of network protocols. Furthermore, the study addresses recent issues and challenges related to IoT-WSNs and proposes mitigation strategies. It provides clear recommendations for the future, emphasizing the integration of advanced technology aiming to contribute to the future development of smart agriculture systems. INDEX TERMSInternet of things, wireless sensor networks, wireless network protocols, smart agriculture applications.

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“…Downsampling on the contrary reduces the training sample count falling under a particular majority class equalizing the count of target categories. But in the process a lot of valuable information gets lost which hinders achievement of accurate results at the end of data analysis [33,34].…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

A PCA-SMO Based Hybrid Classification Model for Predictions in Precision Agriculture

2023

Teh. vjesn.

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The human population is growing at an extremely rapid rate, the demand of food supplies for the survival and sustainability of life is a gleaming challenge. Each living being in the planet gets bestowed with the healthy food to remain active and healthy. Agriculture is a domain which is extremely important as it provides the fundamental resources for survival in terms of supplying food and thus the economy of the entire world is highly dependent on agricultural production. The agricultural production is often affected by various environmental and geographical factors which are difficult to avoid being part of nature. Thus, it requires proactive mitigation plans to reduce any detrimental effect caused by the imbalance of these factors. Precision agriculture is an approach that incorporates information technology in agriculture management, the needs of crops and farming fields are fulfilled to optimized crop health and resultant crop production. The proposed study involves an ambient intelligence-based implementation using machine learning to classify diseases in tomato plants based on the images of its leaf dataset. To analytically evaluate the performance of the framework, a publicly available plant-village dataset is used which is transformed to appropriate form using one-hot encoding technique to meet the needs of the machine learning algorithm. The transformed data is dimensionally reduced by Principal Component Analysis (PCA) technique and further the optimal parameters are selected using Spider Monkey Optimization (SMO) approach. The most relevant features as selected using the Hybrid PCA-SMO technique fed into a Deep Neural Networks (DNN) model to classify the tomato diseases. The optimal performance of the DNN model after implementing dimensionality reduction by Hybrid PCA-SMO technique reached at 99% accuracy was achieved in training and 94% accuracy was achieved after testing the model for 20 epochs. The proposed model is evaluated based on accuracy and loss rate metrics; it justifies the superiority of the approach.

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Classification and yield prediction in smart agriculture system using IoT

Cited by 49 publications

References 29 publications

Internet of Things (IoT)-Enabled Machine Learning Models for Efficient Monitoring of Smart Agriculture

Internet of Things (IoT)-Enabled Machine Learning Models for Efficient Monitoring of Smart Agriculture

Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey

A PCA-SMO Based Hybrid Classification Model for Predictions in Precision Agriculture

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