Soil Fertilizer Recommendation System using Fuzzy Logic

Haban, Jenskie Jerlin I.; Puno, John Carlo V.; Billones, Robert Kerwin C.; Dadios, Elmer P.; Sybingco, Edwin

doi:10.1109/tencon50793.2020.9293780

Cited by 9 publications

(6 citation statements)

References 20 publications

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“…A FLC is a decision-making system that interprets and reacts to input data using fuzzy logic concepts. The inference engine infers the correspondence fuzzy value with the aid of pre-defined fuzzy rules available in the knowledge base, and finally, the defuzzification block converts the fuzzy output value to the corresponding crisp value [10]. The fuzzy logic controller's block design is in Figure 2.…”

Section: Fuzzy Logic Controller (Flc) For Soil Analysismentioning

confidence: 99%

“…It receives input data from an NPK sensor and fuzzify it to translate it into desired language phrases (in Figure 3). The inference engine determines the correspondence fuzzy value using pre-defined fuzzy rules from the knowledge base, which is subsequently translated into a corresponding crisp value by the defuzzification block [10].…”

Section: Fuzzy Logic Controller (Flc) For Soil Analysismentioning

confidence: 99%

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QoS Performance Evaluation for Wireless Sensor Networks: The AQUASENSE Approach

Batsi,

Tennina

2023

The 10th International Electronic Conference on Sensors and Applications

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Section: Fuzzy Logic Controller (Flc) For Soil Analysismentioning

confidence: 99%

Section: Fuzzy Logic Controller (Flc) For Soil Analysismentioning

confidence: 99%

QoS Performance Evaluation for Wireless Sensor Networks: The AQUASENSE Approach

Batsi,

Tennina

2023

The 10th International Electronic Conference on Sensors and Applications

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“…According to the measured Ca, P, and pH levels, these features may help farmers decide how much fertilizer to put into the soil sample to maintain consistent soil fertility [15,18,21]. To increase prediction accuracy, a variety of machine learning techniques, including the Linear Regression algorithm, Artificial Neural Network, Gaussian Naive Bayes, Random Forest method, Gradient Boosting method, K-Nearest Neighbors algorithm, Decision Tree algorithm, and Logistic regression, were tested [23].…”

Section: Related Workmentioning

confidence: 99%

Soil Fertility Detection and Crop Prediction using IoT and Machine Learning

Chaudhari,

Kamble,

Patil

et al. 2023

ijmst

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India has huge agriculture heritage. This is the major source of livelihood for most Indian families. Farmers are seen to use fertilizers in inappropriate proportion to enhance crop yield which results in infertile land. To overcome this issue, to check the fertility level of the soil, environment conditions and predicting suitable crop and fertilizers required is need of hour. Soil fertility depends on nutrients like Nitrogen (N), Phosphorous (P), and Potassium (K). It is also affected by environmental factors such as temperature, moisture, humidity, etc. The proposed system provides a cost-effective solution using IoT and Machine Learning based approach to check the NPK concentration present in the soil. Based on which, user can predict the soil suitable crop. The technique used comprises an integrated light transmission and detection system which consists of three LEDs with different wavelengths. Photodiode (LDR sensor module) is used for light detection purposes. The output obtained from the photodiode is handled using a Arduino UNO microcontroller. Based on the inputs received from LDR module, NPK concentration can be evaluated. The model is trained with the Crop Prediction dataset to predict the crop using LightGBM algorithm. The proportion of NPK nutrients and the predicted crop is sent to the user as a text message through the GSM module and ThingSpeak cloud platform.

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“…The fuzzy system calculates the appropriate amount of fertilizer based on the NPK and season values. The outcome demonstrates that the fuzzy logic system was effectively designed and simulated to provide appropriate fertilizer recommendations [7] . This model incorporates Artificial Neural Networks because they can provide extremely precise results and can evaluate and handle a huge quantity of data on soil moisture, pH, and temperature at various locations quickly, which is critical for any systems that need to produce immediate answers [8] .…”

Section: Literature Reviewmentioning

confidence: 99%

Crop, Fertilizer, & Irrigation Recommendation using Machine Learning Techniques

Ajmera¹,

Bhandari²,

Jain³

et al. 2022

IJRASET

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Agriculture is the majority source of income for many people not just in the Indian subcontinent but around the world and hence forms the backbone of the economy. Present-day difficulties like unpredictability in weather conditions, water scarcity, and volatility due to demand- supply fluctuations create the need for the farmer to be equipped with modern day techniques. More specifically, topics like less yield of crops due to unpredictable climate, faulty irrigation resources, and soil fertility level depletions needto be communicated. Hence there is a requirement to modify the abundant agriculture data into modern day technologies and make them conveniently accessible to farmers. A technique that can be implemented in crop yield predictionis Machine learning. Numerous machine learning techniqueslike regression, clustering, classification and prediction can be employed in crop yield forecasting. Algorithms like Naïve Bayes, support vector machines, decision trees, linear and logistic regression, and artificial neural networks can be employed in the prediction. The wide array of available algorithms poses a selection dilemma with reference to the selected crop. The purpose of this study is to investigate how different machine learning algorithms may be used to forecast agricultural production and present an approach in the context of big data computing for crop yield prediction and fertilizer recommendation using machine learning techniques

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Soil Fertilizer Recommendation System using Fuzzy Logic

Cited by 9 publications

References 20 publications

QoS Performance Evaluation for Wireless Sensor Networks: The AQUASENSE Approach

QoS Performance Evaluation for Wireless Sensor Networks: The AQUASENSE Approach

Soil Fertility Detection and Crop Prediction using IoT and Machine Learning

Crop, Fertilizer, & Irrigation Recommendation using Machine Learning Techniques

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