Smart farming using artificial intelligence: A review

Akkem, Yaganteeswarudu; Biswas, Saroj Kumar; Varanasi, Aruna

doi:10.1016/j.engappai.2023.105899

Cited by 126 publications

(34 citation statements)

References 46 publications

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“…There are many advantages to implementing IoT-based smart irrigation systems at scale, including increased crop yields and water efficiency. The initial cost, infrastructure and connectivity, power supply, data security and privacy, data management, integration complexity, scalability, sensor accuracy and reliability, maintenance and calibration, and environmental factors are some of the challenges and restrictions that must be carefully taken into account [19].…”

Section: Resultsmentioning

confidence: 99%

Agricultural Farm Production Model for Smart Crop Yield Recommendations Using Machine Learning Techniques

Vidhya,

George,

Suresh

et al. 2023

RAiSE-2023

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Section: Resultsmentioning

confidence: 99%

Agricultural Farm Production Model for Smart Crop Yield Recommendations Using Machine Learning Techniques

Vidhya,

George,

Suresh

et al. 2023

RAiSE-2023

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“…Previous studies have primarily utilized artificial intelligence and machine learning technologies for productivity enhancement, disease management, and crop monitoring [1][2][3][4][5][6][7]. Additionally, attempts have been made to use UAVs and spectral images to acquire various biological parameters [17][18][19][20][21].…”

Section: Discussionmentioning

confidence: 99%

“…Hossen et al [4] advocate for AI-driven automation in soil and disease management, crop monitoring, and weed control, highlighting their potential to alleviate agricultural challenges and reduce labor-intensive tasks. Akkem et al [5] review ML and deep learning's relevance in agriculture, emphasizing their role in soil fertility assessment and crop selection, including time-series analysis and prediction. Oliveira and Silva [6] document the prevalent use of AI technologies, including ML, convolutional neural networks, and IoT, in agriculture, while also outlining the future directions and challenges.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Live Bulb Weight for Field Vegetables Using Functional Regression Models and Machine Learning Methods

Kim,

Cho,

et al. 2024

Agriculture

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(1) Background: This challenge is exacerbated by the aging of the rural population, leading to a scarcity of available manpower. To address this issue, the automation and mechanization of outdoor vegetable cultivation are imperative. Therefore, developing an automated cultivation platform that reduces labor requirements and improves yield by efficiently performing all the cultivation activities related to field vegetables, particularly onions and garlic, is essential. In this study, we propose methods to identify onion and garlic plants with the best growth status and accurately predict their live bulb weight by regularly photographing their growth status using a multispectral camera mounted on a drone. (2) Methods: This study was conducted in four stages. First, two pilot blocks with a total of 16 experimental units, four horizontals, and four verticals were installed for both onions and garlic. Overall, a total of 32 experimental units were prepared for both onion and garlic. Second, multispectral image data were collected using a multispectral camera repeating a total of seven times for each area in 32 experimental units prepared for both onions and garlic. Simultaneously, growth data and live bulb weight at the corresponding points were recorded manually. Third, correlation analysis was conducted to determine the relationship between various vegetation indexes extracted from multispectral images and the manually measured growth data and live bulb weights. Fourth, based on the vegetation indexes extracted from multispectral images and previously collected growth data, a method to predict the live bulb weight of onions and garlic in real time during the cultivation period, using functional regression models and machine learning methods, was examined. (3) Results: The experimental results revealed that the Functional Concurrence Regression (FCR) model exhibited the most robust prediction performance both when using growth factors and when using vegetation indexes. Following closely, with a slight distinction, Gaussian Process Functional Data Analysis (GPFDA), Random Forest Regression (RFR), and AdaBoost demonstrated the next-best predictive power. However, a Support Vector Machine (SVM) and Deep Neural Network (DNN) displayed comparatively poorer predictive power. Notably, when employing growth factors as explanatory variables, all prediction models exhibited a slightly improved performance compared to that when using vegetation indexes. (4) Discussion: This study explores predicting onion and garlic bulb weights in real-time using multispectral imaging and machine learning, filling a gap in research where previous studies primarily focused on utilizing artificial intelligence and machine learning for productivity enhancement, disease management, and crop monitoring. (5) Conclusions: In this study, we developed an automated method to predict the growth trajectory of onion and garlic bulb weights throughout the growing season by utilizing multispectral images, growth factors, and live bulb weight data, revealing that the FCR model demonstrated the most robust predictive performance among six artificial intelligence models tested.

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“…Deep learning is one such technique that has several benefits over other pattern recognition techniques. Deep learning models have achieved cutting-edge performance in computer vision [33]- [35], speech recognition [36], [37], and time series classification [38]- [43]. In contrast to traditional machine learning algorithms, DL algorithims such as Convolutional Neural Networks (CNN) with their hierarchical representation, feature learning capabilities, and adaptability to unknown data, stand as a fitting choice for achieving our research objectives.…”

Section: ) Deep Learning Literature Reviewmentioning

confidence: 99%

Open Set Domain Adaptation for Classification of Dynamical States in Nonlinear Fluid Dynamical Systems

Akshay,

Gopalakrishnan,

Sowmya

et al. 2024

IEEE Access

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Nonlinear fluid dynamical systems, such as thermoacoustic systems, aeroelastic systems are archetypical complex systems involving state transitions upon a change in bifurcation parameter. These state transitions in any certain direction are always undesirable and can radically alter the operational paradigms associated with these systems. Hence, predicting the impending dynamical state is paramount for avoiding such undesirable transitions. The hitherto research so far focused largely on metric-based and modelbased indicators to foretell an impending transition and is often fraught with difficulties when deployed in practicable scenarios. In this study, we assuage this end of concern by proposing a model-agnostic datadriven method for automated classification of the dynamical states of nonlinear fluid dynamical systems. By using recurrence plots we transform the time series pertaining to the dynamical states into images and subsequently employ a convolution neural network (CNN) to classify the generated images. This study also proceeds to present cross-domain classifications via a trained deep learning (DL) model and successfully classify the dynamical states of one fluid dynamical system (say, thermoacoustic) with the dynamical states of another fluid dynamical system (say, aeroelastic). The underlying methodology for the above is based on open set (OS) domain adaptation -inherent to transfer learning schemes. Towards enhancing the confidence levels of our proposed methodology, we carry out four cross-domain numerical experiments, wherein we consistently get about 94 -98% accuracy.

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Smart farming using artificial intelligence: A review

Cited by 126 publications

References 46 publications

Agricultural Farm Production Model for Smart Crop Yield Recommendations Using Machine Learning Techniques

Agricultural Farm Production Model for Smart Crop Yield Recommendations Using Machine Learning Techniques

Prediction of Live Bulb Weight for Field Vegetables Using Functional Regression Models and Machine Learning Methods

Open Set Domain Adaptation for Classification of Dynamical States in Nonlinear Fluid Dynamical Systems

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