IoT-based Smart Greenhouse with Disease Prediction using Deep Learning

Fatima, Neda; Siddiqui, Salman; Ahmad, Anwar

doi:10.14569/ijacsa.2021.0120713

Cited by 21 publications

(8 citation statements)

References 18 publications

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“…In order to gain a comprehensive understanding of the use of AI in greenhouse cultivation, the review expanded beyond C. sativa to include a wider range of horticulture-specific AI technologies. Such examples include plant and environment data analysis to recognise patterns and correlations; prediction of optimal harvesting times to improve the quality and yield of crops; and task automation in areas such as monitoring and controlling the growth environment, detecting pests and diseases, and optimising labour resource usage [98], [99], [101], [103], [104], [105], [108], [109]. The applications of AI in greenhouse cultivation are diverse, spanning various use cases, system integrations, and stages of technological readiness.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Survey of Modern Greenhouse Technologies and Practices for Commercial Cannabis Cultivation

et al. 2023

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The limited availability of peer-reviewed scientific evidence in the cannabis industry has led many companies to rely on techniques derived from non-peer-reviewed sources for their practices. This study begins by examining the literature on the cultivation of C. sativa, investigating optimal conditions and their effects on growth, and characterising the requirements for greenhouse monitoring and control. A systematic review of current technological approaches is then conducted. The review demonstrates that technology-based control of greenhouse environments has the potential to surpass manual or traditional rule-based management techniques by reducing costs and increasing yields. However, the adoption of these technologies is impeded by the lack of publicly available labelled data on growth, pest and disease, environmental, and yield data of multiple indoor cultivation cycles. Currently, much of the research in this field is conducted privately by companies in the cannabis industry. This study recognises substantial gaps in research surrounding C. sativa cultivation and emphasises the opportunity for new research to address the absence of available C. sativa datasets and peer-reviewed scientific studies outside of private endeavours.INDEX TERMS AIoT, artificial intelligence (AI), greenhouse cultivation, cannabis, pest and disease detection.

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Section: Literature Reviewmentioning

confidence: 99%

“…From the table, it is evident that most studies focus on monitoring and controlling general parameters such as temperature, light, water, and nutrients [99], [100], [101], [103], [104], [105]. However, there is a lack of AI applications specifically targeting C. sativa cultivation.…”

Section: Literature Reviewmentioning

confidence: 99%

A Survey of Modern Greenhouse Technologies and Practices for Commercial Cannabis Cultivation

et al. 2023

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“…Farmers could observe the monitoring data through a blynk application. In order to quickly identify the vegetable disease, deep learning algorithms were integrated into the smart greenhouse monitoring system to avoid loss in the early stage [16]. With the help of machine learning technology can make the whole smart greenhouse system more intelligent [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Greenhouse Monitoring and Controlling based on NodeMCU

Liu¹

2022

IJACSA

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Food security is one of the major rising issues as the human population is larger and the land available for cultivation is smaller, as well unassured affairs happened often in society especially in the current CoVID-19 rapidly spread days. To mitigate this condition, further improve the yields and quality of food, this paper proposed a smart and low-cost greenhouse monitoring and control system, which mainly consists of sensors, actuators, LCD display and microcontrollers. DHT22 sensor is used to get the surrounding temperature and humidity in the greenhouse, and NodeMCU is used as the main microcontroller. Some other facilities such as fan and heater are used to adjust the inside environment. The system could monitor the growth environment continuously with Internet-connected, the monitoring data is transmitted and stored in the ThingSpeak cloud, the users can visualize the live data through a webpage or phone APP in real-time. If the environment condition is out of the predefined level, the environment is monitored continuously, and the system can be adjusted automatically. This system can be deployed in the greenhouse simply and maintain the greenhouse environment in a normal range dynamically and continuously.

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“…The purpose of this paper is to design an IoT based smart Greenhouse system which monitors, alerts automate and predict disease. [2]. Rajesh Yakkundimath, Girish Saunshi, Vishwanath Kamatar developed Plant Disease Detection using IoT.…”

Section: Related Workmentioning

confidence: 99%

An IoT-Based Model for the Classification of Plant Diseases using Deep Learning

Dhannya¹,

V.Sangeetha²

2022

IJETMS

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In India, Agriculture is of great importance. Diseases affecting crops is a major challenge faced by Agricultural industry. Hence earlier detection of crop disease has greater importance in agricultural field. In recent years, the number of diseases on plants and degree of harm caused has increased due to the variation in the varieties of microorganisms, inadequate cultivation methods and inappropriate plant protection techniques. In the present study, Farmers spend so much money on disease management, often without adequate technical support, resulting in poor disease control, pollution and harmful results. In addition, plant disease can destroy natural ecosystems, adding environmental problems caused by poor management of crops. A symptom of plant disease is a visible effect of disease on the plant. Symptoms may include a visible change in colour, shape or function of the plant as it responds to the pathogen. A faster method for detection of crop diseases is done with the help of IoT. A sensor network can be created in the farm land using Raspberry Pi 4 model. The images will be captured by the sensor cameras and send to the cloud server via Raspberry pi 4 model. The proposed model is a theoretical model. In this proposed methodology, various image processing techniques will be applied on acquired images for classification of crop diseases using fuzzy c-means clustering algorithm. This paper will also shows the method of image processing techniques such as image acquisition, image pre-processing, image segmentation and feature extraction for classification of crop diseases. Farmers can produce quality crops and thus healthy food can be obtained by this proposed methodology and make more profit.

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IoT-based Smart Greenhouse with Disease Prediction using Deep Learning

Cited by 21 publications

References 18 publications

A Survey of Modern Greenhouse Technologies and Practices for Commercial Cannabis Cultivation

A Survey of Modern Greenhouse Technologies and Practices for Commercial Cannabis Cultivation

Smart Greenhouse Monitoring and Controlling based on NodeMCU

An IoT-Based Model for the Classification of Plant Diseases using Deep Learning

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