sCrop: A Novel Device for Sustainable Automatic Disease Prediction, Crop Selection, and Irrigation in Internet-of-Agro-Things for Smart Agriculture

Udutalapally, Venkanna; Mohanty, Saraju P.; Pallagani, Vishal; Khandelwal, Vishal

doi:10.1109/jsen.2020.3032438

Cited by 73 publications

(20 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The IoT‐Irrigation is a dynamic irrigation scheduling system (AgriSens) to manage water for agricultural land. AgriSens provides real‐time, automated, and dynamic processing at various stages of a crop life cycle using populations 7 such as remote manual irrigation.…”

Section: Related Workmentioning

confidence: 99%

Retracted: Adaptive neuro fuzzy inference system to enhance the classification performance in smart irrigation system

Vivekanandhan¹,

Sakthivel²,

Manikandan³

2021

Computational Intelligence

View full text Add to dashboard Cite

Traditional agricultural systems require huge amounts of money for on‐site irrigation power. Irrigation is the process of giving water to plants for growth and development. The cost of global warming creates the potential effects of water adaptation measures to ensure food production and water availability for consumption. It requires continuous monitoring of soil moisture content at the root zone and initiating irrigation as per the pre‐programmed schedule depending upon the plant's nature, growth, soil type, and environment. Thus, the cost to farmers using conventional drip irrigation has become so high that they have to go manually and monitor the land frequently. So when the previous method was used for irrigation, it did not give correct classification results. Agricultural data mining technology provides the best crops for travel water to increase the number of crops produced. Agricultural data can be collected more efficiently using data collection and capable analytics sensors. This proposed work introduces an adaptive neuro‐fuzzy inference system (ANFIS) technique for analyzing agricultural plant growth based on soil, water level, temperature, and moisture conditions. The proposed technique has three phases: environment data collection using sensor node, preprocessing, feature selection, and classification. The initial phase captures the plant's environment condition via a sensor device, then transmits data to servers with the help of an edge node or gateway. This recursive adaptive filter method normalizes the data and removes irrelevant noise data from the sensor node. The feature selection method to extract the environment feature value with the help of a linear regression model. The ANFIS method's plant environment feature value has trained the neurons with the help of the stochastic gradient descent method. The classification method introduces a fuzzy rule to compute and validate the input parameters (e.g., soil moisture, temperature, and humidity) to predict any environment value changes. The comparison results prove that efficient monitoring is obtained through the proposed smart irrigation system.

show abstract

Section: Related Workmentioning

confidence: 99%

Retracted: Adaptive neuro fuzzy inference system to enhance the classification performance in smart irrigation system

Vivekanandhan¹,

Sakthivel²,

Manikandan³

2021

Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…Pest detection and recognition have been performed through k-means clustering and correspondence filter [149]. CNN based models have been used in [150] and in [151] in crop disease detection.…”

Section: Pest/disease Controlmentioning

confidence: 99%

Everything You wanted to Know about Smart Agriculture

Mitra¹,

Vangipuram²,

Bapatla³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…The experimental results showed that this method achieved good results in the corn leaf database. Udutalapally et al proposed a method to distinguish wheat pest leaves based on imaging hyperspectral technology from the perspective of optics, constructed a spectral ratio fingerprint feature based on the relative change of spectra, preferentially selected several imagebased geometric and texture features in a targeted manner, and achieved very promising recognition ability for wheat in the experiments [8].…”

Section: Related Workmentioning

confidence: 99%

Image Detection System Based on Smart Sensor Network and Ecological Economy in the Context of Fine Agriculture

Wang

Teo

et al. 2022

Journal of Sensors

View full text Add to dashboard Cite

In this paper, an in-depth study and analysis of the ecological economy of fine agriculture are carried out using image detection methods of smart sensor networks. The analog signal output from the wireless sensor network is filtered and thresholder to convert into a digital signal to complete the sensor monitoring data preprocessing for digital information analysis. In this paper, with the objectives of good environmental adaptability, low power consumption, low cost, and standardization, the key technologies of wireless sensor networks for fine agriculture are studied, including network structure, networking method, node positioning method, data fusion method, rapid energy self-sufficiency, and energy-saving strategy, and the performance evaluation method of wireless sensor network system, IoT-oriented middleware design method, generic node software and hardware design method, and typical application system. Firstly, a convolutional layer is used instead of a fully connected layer, which makes the network more flexible in terms of input image requirements and enables the calculation of the target rice region. Not only will many complex operations be generated, but it will also limit the generalization ability of the model. Then, by introducing a flexible connection layer based on unit and optimizing the loss function of the network, a crop convolutional neural network (Crop-Net) is finally proposed for training and testing rice images at different growth stages to improve the detection accuracy. In this paper, a network quality of service goal-driven evaluation strategy and evaluation method for agricultural wireless sensor network systems is designed to provide a reference for the establishment of industry standards for wireless sensor network systems for fine agriculture.

show abstract

sCrop: A Novel Device for Sustainable Automatic Disease Prediction, Crop Selection, and Irrigation in Internet-of-Agro-Things for Smart Agriculture

Cited by 73 publications

References 29 publications

Retracted: Adaptive neuro fuzzy inference system to enhance the classification performance in smart irrigation system

Retracted: Adaptive neuro fuzzy inference system to enhance the classification performance in smart irrigation system

Everything You wanted to Know about Smart Agriculture

Image Detection System Based on Smart Sensor Network and Ecological Economy in the Context of Fine Agriculture

Contact Info

Product

Resources

About