IoT-Equipped and AI-Enabled Next Generation Smart Agriculture: A Critical Review, Current Challenges and Future Trends

Qazi, Sameer; Khawaja, Bilal A.; Farooq, Qazi Umar

doi:10.1109/access.2022.3152544

Cited by 158 publications

(62 citation statements)

References 107 publications

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“…An optical sensor equipped with it can easily capture clear three-dimensional (3D) resolution images. The vertical takeoff and landing (VTOL) system in UAVs is a recent development that allows ease in maneuvering [64]. VTOL UAVs also remain unaffected by the attributes of forests and climatic conditions.…”

Section: Recent Technologies That Handle Effects Of Forest Attributesmentioning

confidence: 99%

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

Ali

Yang

Israr

et al. 2023

Drones

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Disease detection in plants is essential for food security and economic stability. Unmanned aerial vehicle (UAV) imagery and artificial intelligence (AI) are valuable tools for it. The purpose of this review is to gather several methods used by our peers recently, hoping to provide some knowledge and assistance for researchers and farmers so that they can employ these technologies more advantageously. The studies reviewed in this paper focused on Scab detection in Rosaceae family fruits. Feature extraction, segmentation, and classification methods for processing the UAV-obtained images and detecting the diseases are discussed briefly. The advantages and limitations of diverse kinds of UAVs and imaging sensors are also explained. The widely applied methods for image analysis are machine learning (ML)-based models, and the extensively used UAV platforms are rotary-wing UAVs. Recent technologies that cope with challenges related to disease detection using UAV imagery are also detailed in this paper. Some challenging issues such as higher costs, limited batteries and flying time, huge and complex data, low resolution, and noisy images, etc., still require future consideration. The prime significance of this paper is to promote automation and user-friendly technologies in Scab detection.

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Section: Recent Technologies That Handle Effects Of Forest Attributesmentioning

confidence: 99%

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

Ali

Yang

Israr

et al. 2023

Drones

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“…According to a well-known forecast, there would be more than 9 billion people in 2050. Fortunately, owing to quickly emerging technology and IoT applications [2] for smart farming, there is still hope. According to analysts, this industry will reach 23.14 billion US dollars by 2022, with 75 million IoT devices implemented for agricultural applications in the following several years.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Smart Hydroponics Farming Using IoT‐Based AI Controller with Mobile Application System

Raju¹,

Dappuri²,

Varma

et al. 2022

Journal of Nanomaterials

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Hydroponics is the soil less agriculture farming, which consumes less water and other resources as compared to the traditional soil-based agriculture systems. However, monitoring of hydroponics farming is a challenging task due to the simultaneous supervising of numerous parameters, nutrition suggestion, and plant diagnosis system. But the recent technological developments are quite useful to solve these problems by adopting the artificial intelligence-based controlling algorithms in agriculture sector. Therefore, this article focuses on implementation of mobile application integrated artificial intelligence based smart hydroponics expert system, hereafter referred as AI-SHES with Internet of Things (IoT) environment. The proposed AI-SHES with IoT consists of three phases, where the first phase implements hardware environment equipped with real-time sensors such as NPK soil, sunlight, turbidity, pH, temperature, water level, and camera module which are controlled by Raspberry Pi processor. The second phase implements deep learning convolutional neural network (DLCNN) model for best nutrient level prediction and plant disease detection and classification. In third phase, farmers can monitor the sensor data and plant leaf disease status using an Android-based mobile application, which is connected over IoT environment. In this manner, the farmer can continuously track the status of his field using the mobile app. In addition, the proposed AI-SHES also develops the automated mode, which makes the complete environment in automatic control manner and takes the necessary actions in hydroponics field to increase the productivity. The obtained simulation results on disease detection and classification using proposed AI-SHES with IoT disclose superior performance in terms of accuracy, F-measure with 99.29%, and 99.23%, respectively.

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“…As a result, AI can provide new decisions for farmers. AI works very effectively and efficiently in analyzing the current trends [35].…”

Section: Introductionmentioning

confidence: 99%

“…Study [37] has taken several into account: plant classification, disease detection, precision breeding, land cover identification, object recognition of pests, weed detection, phenotype, and smart irrigation. Other studies [35], [38] focus on research in agriculture by conducting systematic reviews on future trends, challenges, etc.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Current Trends in Artificial Intelligence for Smart Farming to Enhance Crop Yield

Widianto

Ardimansyah²,

Pohan³

et al. 2022

Journal of Robotics and Control

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Current technology has been widely applied for development, one of which has an Artificial Intelligence (AI) applied to Smart Farming. AI can give special capabilities to be programmed as needed. In cooperation with agricultural systems, AI is part of improving the quality of agriculture. This technology is no stranger to being applied in basic fields such as agriculture. This smart technology is needed to increase crop yields for various regions by utilizing the current trends paper. This is necessary because less land is available for agriculture, and there is a greater need for food sources. Therefore, this systematic review aims to collect the current trends in AI studies for Smart Farming papers using the latest year features from 2018-2022. This paper is handy for researchers and industry in looking for the latest papers on research to enhance crop yields. The authors utilized Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) of 534 articles from IEEE, ACM, MDPI, IAES, and ScienceDirect. After going through a careful process, 67 papers were found that were judged according to the criteria. After the authors got some of the current trends, the author has discussed several factors regarding the results obtained to enhance crop yields, such as Weather, Soil, Irrigation, Unmanned Aerial Vehicle (UAV), Pest Control, Weed Control, and Disease Control.

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IoT-Equipped and AI-Enabled Next Generation Smart Agriculture: A Critical Review, Current Challenges and Future Trends

Cited by 158 publications

References 107 publications

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

A Comprehensive Review of Scab Disease Detection on Rosaceae Family Fruits via UAV Imagery

Design and Implementation of Smart Hydroponics Farming Using IoT‐Based AI Controller with Mobile Application System

A Systematic Review of Current Trends in Artificial Intelligence for Smart Farming to Enhance Crop Yield

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