Response of maize to irrigation and blended fertilizer levels for climate smart food production in Wolaita Zone, southern Ethiopia

Chinasho, Alefu; Bedadi, Bobe; Lemma, Tesfaye; Tana, Tamado; Hordofa, Tilahun; Elias, Bisrat

doi:10.1016/j.jafr.2023.100551

Cited by 8 publications

(3 citation statements)

References 39 publications

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“…Another study the smart irrigation systems were installed in a sugarcane field. The study found that the use of smart irrigation systems resulted in a 40% reduction in water usage and a 33% increase in sugarcane yield compared to traditional irrigation methods [9], [12].…”

Section: Introductionmentioning

confidence: 98%

“…By reducing water wastage, smart irrigation systems can help to conserve water resources, which is critical in areas where water is scarce. Smart irrigation systems can also reduce the use of fertilizers and pesticides, as they enable the precise application of water, which can reduce the leaching of nutrients and chemicals from the soil [9]. This can help to prevent pollution of water sources, such as rivers and lakes.…”

Section: Introductionmentioning

confidence: 99%

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An IoT-Based System for Managing and Monitoring Smart Irrigation through Mobile Integration

Rane

et al. 2023

JMC

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The agricultural sector plays a significant role in the economy of many countries, and irrigation is a critical component of successful agriculture. However, traditional irrigation methods can be time-consuming and labor-intensive, and often result in the over or under-watering of crops, which can negatively impact crop yields. To overcome these challenges, smart irrigation systems have been developed to assist farmers in managing their crops and increasing their yield. This research article presents an IoT-based smart irrigation system that uses four sensors - moisture content, temperature, humidity, and ultrasonic - to collect data from the irrigation area and transmit it to a central control system. The central control system uses the data to automatically turn the irrigation pump on and off, based on the moisture level of the soil. The system also includes a mobile application that allows farmers to monitor the system remotely and control the motor pump from their smartphones. The proposed system has several advantages, including reducing the hard work of farmers, providing essential strength to crops, and ensuring that plants receive the adequate amount of water at the required time. Additionally, the system's remote monitoring capabilities allow farmers to monitor the atmospheric temperature, humidity, and moisture content from anywhere at any time, and make adjustments as necessary. Overall, the findings of this research will help farmers to control their irrigation systems remotely, reduce labor costs, and increase crop yields. By improving the efficiency of irrigation and reducing water waste, this IoT-based smart irrigation system has the potential to significantly impact the agriculture sector and promote sustainable farming practices.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

An IoT-Based System for Managing and Monitoring Smart Irrigation through Mobile Integration

Rane

et al. 2023

JMC

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“…Water management is one of the central aspects of precision agriculture: uneven irrigation often results in significant inefficiencies and waste of resources. Although the moisture of the soil and crop needs vary depending on the type of plant and external conditions, traditional form of irrigation do not allow due precision of water allocation [4]- [6]. Recognizing the potential waste of valuable resources, timely switching on of water supply, or, in contrast, late irrigation, it becomes apparent that intelligent water management is required.…”

Section: Introductionmentioning

confidence: 99%

Machine learning and Sensor-Cloud Based Precision Agriculture for Intelligent Water Management for Enhanced Crop Productivity

Abhishek Sharma

2024

jes

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The combination of Machine Learning algorithms with Internet of Things devices is emerging as an effective solution to redefining precision agriculture for better water management and crop cultivation. The purpose of this study is to use different learning models, such as Artificial Neural Networks , Support Vector Machines , Decision Trees , and Random Forest , to predict the irrigation need based on real-world sensor data. To retrieve the output variable, which is the irrigation requirement, data from temperature, soil moisture, water level, and humidity sensors that are available in a tomato cultivation facility are used. The dataset consists of 3422 readings, which are split into training and testing sets. A designated percentage of 70% is used for training the models, while the remaining 30% are used to test the outcomes. As the results of the study show, the ANN model is the most accurate predictor of irrigation need with the classification rate of 97.6%, followed by SVM , DT , and RF with 95.4%, 91.3%, and 88.9% correspondingly. The differences in the outcomes are demonstrated in confusion matrices, which identify the classification of the cases and indicate the percentage of correct predictions. Evidence of the predictive power of ML models implies that farmers can independently determine when to activate the pump when real-world data serve as the input. Additionally, the ability to collect real-world data using IoT sensors is beneficial, as it empowers farmers with up-to-date information to make a timely decision about pump activation. The limitations are associated with the type of crop and agricultural facility and, for this reason, future studies may investigate the generality of the conclusion with regard to other crop types and facilities.

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Enhancing eggplant (Solanum melongena L.) yield and water use efficiency through optimized irrigation and nitrogen practices in open field conditions

Bello,

Huda,

Alsafran

et al. 2024

Journal of Agriculture and Food Research

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Response of maize to irrigation and blended fertilizer levels for climate smart food production in Wolaita Zone, southern Ethiopia

Cited by 8 publications

References 39 publications

An IoT-Based System for Managing and Monitoring Smart Irrigation through Mobile Integration

An IoT-Based System for Managing and Monitoring Smart Irrigation through Mobile Integration

Machine learning and Sensor-Cloud Based Precision Agriculture for Intelligent Water Management for Enhanced Crop Productivity

Enhancing eggplant (Solanum melongena L.) yield and water use efficiency through optimized irrigation and nitrogen practices in open field conditions

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