A comparative study of deep learning and Internet of Things for precision agriculture

Saranya, T.; Deisy, C.; Sridevi, S.; Anbananthen, Kalaiarasi Sonai Muthu

doi:10.1016/j.engappai.2023.106034

Cited by 69 publications

(36 citation statements)

References 71 publications

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“…The summary of the different IoT frameworks, models, and machinelearning algorithms is presented in Table 1. The authors of [25] presente a comparative study for precision agriculture using deep learning and IoT. In this work, authors have gathered and analysed disease, weeds, and soil yields using deep learning techniques.…”

Section: Irrigation Techniquesmentioning

confidence: 99%

Sustainable Irrigation Requirement Prediction Using Internet of Things and Transfer Learning

Blessy

Kumar

Prabagaran³

et al. 2023

Sustainability

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Irrigation systems are a crucial research area because it is essential to conserve fresh water and utilize it wisely. As a part of this study, the reliability of predicting the usage of water in the present and future is investigated in order to develop an effective prediction model to communicate demand. In order to improve prediction, we develop a prediction model and share the updated model with nearby farmers. In order to forecast the irrigation requirements, the recommended model utilizes the Internet of Things (IoT), k-nearest neighbours (KNN), cloud storage, long short-term memory (LSTM), and adaptive network fuzzy inference system (ANFIS) techniques. By collecting real-time environmental data, KNN identifies the closest water requirement from the roots and its surrounding. In order to predict short-term requirements, ANFIS is used. To transfer the new requirements for better prediction, transfer learning is used. Time-series-data updates are predicted using LSTM for future forecasting, and the integrated model is shared with other farmers using cloud environments to enhance forecasting and analysis. For implementation, a period of nine to ten months of data was collected from February to December 2021, and banana tree was used to implement the planned strategy. Four farms, with measurements, were considered at varying intervals to determine the minimum and maximum irrigation needs. The requirements of farms were collected over time and compared to the predictions. Future requirements at 8, 16, 24, 32, and 48 h were also anticipated. The results indicated were compared to manual water pouring, and, thus, the entire crop used less water, making our prediction model a real-world option for irrigation. The prediction model was evaluated using R2, MSLE and the average initial prediction value of R2 was 0.945. After using transfer learning, the prediction of the model of Farm-2, 3 and 4 were 0.951, 0.958 and 0.967, respectively.

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Section: Irrigation Techniquesmentioning

confidence: 99%

Sustainable Irrigation Requirement Prediction Using Internet of Things and Transfer Learning

Blessy

Kumar

Prabagaran³

et al. 2023

Sustainability

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“…The maize crop's production ratio has been severely effected by the occurrence of various diseases, such as bacterial stalk root, which, if not controlled in its earliest stages, could possibly damage an entire field or farm. 2,4 Thus, technology-supported infrastructures, that is, those particularly designed for the agriculture domain (the maize crop), which must be cheaper, are required to be developed and implemented in a real agricultural environment.…”

Section: Introductionmentioning

confidence: 99%

“…Although the introduction of smart technology, that is, hybrids of IoT and AI, has improved the production ratio of various crops such as wheat and graphs, disease (if it occurs) could possibly decrease the production ratio of these crops. The maize crop's production ratio has been severely effected by the occurrence of various diseases, such as bacterial stalk root, which, if not controlled in its earliest stages, could possibly damage an entire field or farm 2,4 . Thus, technology‐supported infrastructures, that is, those particularly designed for the agriculture domain (the maize crop), which must be cheaper, are required to be developed and implemented in a real agricultural environment.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence and Internet of Things‐enabled decision support system for the prediction of bacterial stalk root disease in maize crop

Al‐Otaibi,

Khan,

Ali

et al. 2024

Computational Intelligence

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Although the Internet of Things (IoT) has been considered one of the most promising technologies to automate various daily life activities, that is, monitoring and prediction, it has become extremely useful for problem solving with the introduction and integration of artificial intelligence (AI)‐enabled smart learning methodologies. Therefore, due to their overwhelming characteristics, AI‐enabled IoTs have been used in different application environments, such as agriculture, where detection, prevention (if possible), and prediction of crop diseases, especially at the earliest possible stage, are desperately required. Bacterial stalk root is a common disease of tomatoes that severely affects its production and yield if necessary measures are not taken. In this article, AI and an IoT‐enabled decision support system (DSS) have been developed to predict the possible occurrence of bacterial stalk root diseases through a sophisticated technological infrastructure. For this purpose, Arduino agricultural boards, preferably with necessary embedded sensors, are deployed in the agricultural field of maize crops to capture valuable data at a certain time interval and send it to a centralized module where AI‐based DSS, which is trained on an equally similar data set, is implemented to thoroughly examine captured data values for the possible occurrence of the disease. Additionally, the proposed AI‐ and IoT‐enabled DSS has been tested on benchmark data sets, that is, freely available online, along with real‐time captured data sets. Both experimental and simulation results show that the proposed scheme has achieved the highest accuracy level in timely prediction of the underlined disease. Finally, maize crop plots with the proposed system have significantly increased the yield (production) ratio of crops.

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“…Agricultural production methods in countries around the world are gradually transitioning to modern agriculture. Precision agriculture (PA), as the development direction of modern agriculture, has attracted the attention of many scholars [1]. How * Author to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

A novel microwave Doppler method for mass estimation of machine-picked seed cotton

Zhang,

Feng,

Liu

et al. 2024

Meas. Sci. Technol.

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Obtaining information on cotton yields and creating a spatial map of cotton production is the starting point for implementing precision agriculture. Microwave technology has been used for yield estimation in China for crops such as corn and soybeans and has shown strong applicability. However, research on cotton yield estimation using microwave technology is weak. Therefore, this paper proposes a method for seed cotton mass estimation based on the microwave Doppler method and designs bench tests to prove the feasibility of the method. First, a seed cotton speed estimation model was established based on the microwave Doppler effect and fast Fourier transform. On this basis, a seed cotton velocity estimation experiment was designed to study the effects of sensor installation angle and position, pipe collision friction, air resistance, and the interaction force between the seed cotton on the seed cotton movement velocity. The power spectral density (PSD) of the acquired microwave data is then estimated using the modified periodogram method. The least squares method was used to establish a linear regression model between seed cotton mass and the corresponding PSD, and finally the free-fall mass experiment and pneumatic conveying mass experiment yielded that the estimation errors of seed cotton mass were 7.30% and 6.17%, respectively. The experimental results show that the estimation error is small when using microwave technology for seed cotton mass estimation, which meets the requirements of actual operation.

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A comparative study of deep learning and Internet of Things for precision agriculture

Cited by 69 publications

References 71 publications

Sustainable Irrigation Requirement Prediction Using Internet of Things and Transfer Learning

Sustainable Irrigation Requirement Prediction Using Internet of Things and Transfer Learning

Artificial intelligence and Internet of Things‐enabled decision support system for the prediction of bacterial stalk root disease in maize crop

A novel microwave Doppler method for mass estimation of machine-picked seed cotton

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