Web-based intelligent system for predicting apricot yields using artificial neural networks

Blagojević, Marija; Blagojević, Miladin; Ličina, V.

doi:10.1016/j.scienta.2016.10.032

Cited by 12 publications

(2 citation statements)

References 20 publications

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“…Exploratory data analysis was performed before using the abovementioned machine learning methods, and it was concluded that the best performance was shown by integrating climate and satellite data. Blagojevic et al [88] studied apricot in a research work where PDCA (Plan, Do, Check, Act) method was employed for predicting the yield. An Artificial Neural Network, as a machine learning method, was used for the abovementioned predictions that took as input: shoot length, fruit weight, shoot thickness, amount of fertilizer, and beginning of the harvest.…”

Section: Yield Prediction In Smart Agriculturementioning

confidence: 99%

Application of Smart Techniques, Internet of Things and Data Mining for Resource Use Efficient and Sustainable Crop Production

et al. 2023

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Technological advancements have led to an increased use of the internet of things (IoT) to enhance the resource use efficiency, productivity, and cost-effectiveness of agricultural production systems, particularly under the current scenario of climate change. Increasing world population, climate variations, and propelling demand for the food are the hot discussions these days. Keeping in view the importance of the abovementioned issues, this manuscript summarizes the modern approaches of IoT and smart techniques to aid sustainable crop production. The study also demonstrates the benefits of using modern IoT approaches and smart techniques in the establishment of smart- and resource-use-efficient farming systems. Modern technology not only aids in sustaining productivity under limited resources, but also can help in observing climatic variations, monitoring soil nutrients, water dynamics, supporting data management in farming systems, and assisting in insect, pest, and disease management. Various type of sensors and computer tools can be utilized in data recording and management of cropping systems, which ensure an opportunity for timely decisions. Digital tools and camera-assisted cropping systems can aid producers to monitor their crops remotely. IoT and smart farming techniques can help to simulate and predict the yield production under forecasted climatic conditions, and thus assist in decision making for various crop management practices, including irrigation, fertilizer, insecticide, and weedicide applications. We found that various neural networks and simulation models could aid in yield prediction for better decision support with an average simulation accuracy of up to 92%. Different numerical models and smart irrigation tools help to save energy use by reducing it up to 8%, whereas advanced irrigation helped in reducing the cost by 25.34% as compared to soil-moisture-based irrigation system. Several leaf diseases on various crops can be managed by using image processing techniques using a genetic algorithm with 90% precision accuracy. Establishment of indoor vertical farming systems worldwide, especially in the countries either lacking the supply of sufficient water for the crops or suffering an intense urbanization, is ultimately helping to increase yield as well as enhancing the metabolite profile of the plants. Hence, employing the advanced tools, a modern and smart agricultural farming system could be used to stabilize and enhance crop productivity by improving resource use efficiency of applied resources i.e., irrigation water and fertilizers.

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Section: Yield Prediction In Smart Agriculturementioning

confidence: 99%

Application of Smart Techniques, Internet of Things and Data Mining for Resource Use Efficient and Sustainable Crop Production

et al. 2023

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“…In their review, [10] discussed the application of smart technology, the internet of Things (IoT), and data recording in agricultural production systems . They explored innovative E3S Web of Conferences 469, 00038 (2023) ICEGC'2023 https://doi.org/10.1051/e3sconf/202346900038 techniques to combat climate change and maintain sustainable crops, such as regression model [16], neural network [17], [18], fuzzy logic [19], [20], and linear regression [21] for soil moisture, seeding, yield predictions, and irrigation management. Image processing techniques and genetic algorithms were also discussed for smart disease management, achieving precision accuracy up to 97.2% [22].…”

Section: Greenhouse Monitoring and Control Systemmentioning

confidence: 99%

Design and Remote Monitoring of a wireless-Controlled Smart Agricultural Greenhouse

Abou-Mehdi-Hassani,

Ait Bouh,

Boudnaya

et al. 2023

E3S Web of Conf.

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This article presents a wirelessly controlled smart agriculture system and offers a comprehensive literature review to explore advancements in greenhouse monitoring and control systems. The system utilizes various sensors and actuators to optimize climatic conditions and enhance the growth of Dianthus Caryophyllus (CLOVE), an aromatic plant. Developed using Internet of Things (IoT) technology, the smart greenhouse system continuously monitors crucial environmental variables, including temperature, humidity, CO2 levels, and soil moisture. The collected data is utilized by actuators to make real-time adjustments to the greenhouse environment. The prototype operates through wireless communication protocols accessible via a dedicated mobile application.

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Early Prediction of Student Success Based on Data Mining and Artificial Neural Network

Bursac¹,

Blagojević

Milošević

2019

Human Centered Computing

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Web-based intelligent system for predicting apricot yields using artificial neural networks

Cited by 12 publications

References 20 publications

Application of Smart Techniques, Internet of Things and Data Mining for Resource Use Efficient and Sustainable Crop Production

Application of Smart Techniques, Internet of Things and Data Mining for Resource Use Efficient and Sustainable Crop Production

Design and Remote Monitoring of a wireless-Controlled Smart Agricultural Greenhouse

Early Prediction of Student Success Based on Data Mining and Artificial Neural Network

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