Fertilizer Recommendation System Research Articles

Numerical Data Based Fertilizer Recommendation System for Farmer

In modern agriculture, optimizing fertilizer use is crucial for maximizing crop yields, maintaining soil health, and reducing environmental impact. The Numerical Data-Based Fertilizer Recommendation System (NDFRS) for farmers addresses these needs by leveraging data-driven approaches to provide precise fertilizer recommendations tailored to specific crop and soil conditions. This system integrates soil nutrient analysis, crop nutrient requirements, and environmental factors to generate customized fertilizer plans. The NDFRS employs advanced machine learning algorithms to analyze extensive datasets, including soil test results, historical crop yield data, weather patterns, and geographical information. By incorporating these variables, the system can predict the optimal type and amount of fertilizers required for different crops at various growth stages. This ensures that nutrients are supplied efficiently, enhancing crop productivity while minimizing excess fertilizer use and associated environmental risks. The user- friendly interface of the NDFRS allows farmers to input relevant data easily and receive actionable recommendations. Additionally, the system offers real-time updates and adaptive recommendations based on changing environmental conditions and crop growth stages. Field trials and validation studies demonstrate the system's efficacy in improving crop yields and promoting sustainable farming practices. Keywords: Tensor Flow, Deep Learning.

Numerical Data Based Fertilizer Recommendation System for Farmer

Numerical Data Based Fertilizer Recommendation System for Farmer

Rootrise - Make Automate Agriculture (Crop Prediction, Crop Disease Detection and Fertilizer Recommendation)

Abstract: Agricultural productivity and sustainability are vital for global food security, but challenges like soil degradation, crop diseases, and inefficient fertilizer use hinder crop quality and yield. To address these, advanced technologies like machine learning and AI are increasingly used in agriculture. This review focuses on recent advances in soil prediction, crop disease prediction, and fertilizer recommendation systems.

Sustainable Food Development Based on Ensemble Machine Learning Assisted Crop and Fertilizer Recommendation System

Agriculture is the most vital sector for the global food supply, and it also provides raw materials for other types of industries. A crop recommendation system is essential for farmers who want to get the most out of their crop-choosing decisions. Over the last several decades, the world's ability to produce food has grown substantially owing to the extensive usage of fertilizers. Therefore, there has to be a more eco-friendly and effective way to utilize fertilizers that include nitrogen (N), phosphorous (P), and potassium (K) to ensure food security. For the reason, this study proposes an ensemble machine learning–assisted crop and fertilizer recommendation system (EML–CFRS) to maximize agricultural output while ensuring the correct use of mineral resources. The research used a dataset obtained from the Kaggle repository like that people can assess several distinct ML algorithms. The databases include data on three climate variables—temperature, rainfall, and humidity—and information on NPK and soil pH. The yields agricultural crops were used to train these models, including Decision Tree, KNN, XGBoost, Support Vector Machine, and Random Forest. Depending on the current weather and soil conditions, the trained model may then recommend the optimal fertiliser for a certain crop. Predicting the ideal kind and quantity of fertilizer for different crops was accomplished with a 96.5% accuracy rate by our suggested strategy.

Smart Cultivation with IoT Monitoring and Fertilizer Recommendation System with Climatic Conditions

Plants are one of the most crucial survival needs of our life. Apart from providing us with food, they also maintain the carbon dioxide and oxygen levels in air. Most of the plants are dying due to poor maintenance, lack of moisture, improper temperature levels etc. This is where IoT should be introduced to the present gardening system. A smart gardening system can be introduced to the traditional gardening system which senses the requirements of the plant and provides it with water as the soil loses its moisture. Using Soil Moisture sensor, which detects the moisture levels of the soil, one can know whether to turn on the motor (water the plants) or not. Using BMP280, information like temperature, humidity can be sensed. The information about the moisture levels, temperature and humidity is updated to a real-time database in firebase which in turn updates the mobile application. The mobile application can be used for monitoring and controlling which is integrated with the sensors using IoT. The mobile application is built with the help of Kodular, a no-code application development interface. This system is further enhanced to run without human intervention by automating the motor switch. This integration is easy to use, burden free smart monitoring and an efficient way of maintaining a plant’s health.

Farmers’ Toolkit: Deep Learning in Weed Detection and Precision Crop & Fertilizer Recommendations

Agriculture is widely recognized as a significant and indispensable occupation on a global scale. The current imperative is to optimize agricultural practices and progressively transition towards smart agriculture. The Internet of Things (IoT) technology has dramatically enhanced people’s daily lives via diverse applications across several domains. Previous studies have yet to effectively incorporate Artificial Intelligence (AI) with sensor technology to provide comprehensive guidance to agricultural practitioners, hindering their ability to achieve good outcomes. This research offers Farmers’ Toolkit with four layers: sensor, network, service, and application. This toolkit aims to facilitate the implementation of a smart farming system while effectively managing energy resources. With a specific emphasis on the application layer, the toolkit uses a deep learning methodology to construct a fertilizer recommendation system that aligns with the expert’s perspective. This study utilizes IoT devices and Wireless Sensor Network (WSN) methods to enhance the efficiency and speed of recommending appropriate crops to farmers. The recommendation process considers several criteria: temperature, yearly precipitation, land area, prior crop history, and available resources. The identification of undesirable vegetation on agricultural fields, namely the detection of weeds, is carried out using drone technology equipped with frame-capturing capabilities and advanced deep-learning algorithms. The findings demonstrate an accuracy rate of 94%, precision rate of 92%, recall rate of 96%, and F1 score of 94%. The toolkit for farmers alleviates physical labor and time expended on various agricultural tasks while enhancing overall land productivity, mitigating potential crop failures in specific soil conditions, and minimizing crop damage inflicted by weeds.

Data-Driven Analysis and Machine Learning-Based Crop and Fertilizer Recommendation System for Revolutionizing Farming Practices

Agriculture plays a key role in global food security. Agriculture is critical to global food security and economic development. Precision farming using machine learning (ML) and the Internet of Things (IoT) is a promising approach to increasing crop productivity and optimizing resource use. This paper presents an integrated crop and fertilizer recommendation system aimed at optimizing agricultural practices in Rwanda. The system is built on two predictive models: a machine learning model for crop recommendations and a rule-based fertilization recommendation model. The crop recommendation system is based on a neural network model trained on a dataset of major Rwandan crops and their key growth parameters such as nitrogen, phosphorus, potassium levels, and soil pH. The fertilizer recommendation system uses a rule-based approach to provide personalized fertilizer recommendations based on pre-compiled tables. The proposed prediction model achieves 97% accuracy. The study makes a significant contribution to the field of precision agriculture by providing decision support tools that combine artificial intelligence and domain knowledge.

Revolutionizing Agriculture: Machine Learning-Driven Crop Recommendations and Disease Detection in Fertilizer Management

Modern agriculture faces a multitude of challenges, including crop failures, disease outbreaks, and suboptimal yields, primarily stemming from the underutilization of advanced farming technologies and a lack of expert guidance. This research proposes a comprehensive solution consisting of three key components: a Crop Disease Detection System, a Fertilizer Recommendation System, and a Crop Suggestion System. The Crop Disease Detection System employs state-of-the-art technology to evaluate crop health by analyzing the condition of plant leaves, enabling early and accurate identification of agricultural diseases. Simultaneously, the Fertilizer Recommendation System leverages soil quality data and environmental factors to provide personalized fertilizer recommendations, optimizing nutrient application. An essential element of this system is a robust soil testing module, recognizing the critical importance of assessing soil quality. Soil fertility evaluation, guided by soil pH measurements, enables precise crop predictions. The proposed system utilizes Machine Learning classification algorithms to predict suitable crops based on essential soil parameters—Phosphorus, Potassium, and Nitrogen levels. It also offers tailored fertilizer recommendations to enhance soil fertility. By implementing these interconnected solutions, this research aims to significantly improve crop yields while reducing crop damage. This holistic approach empowers farmers with the tools and knowledge needed to enhance agricultural productivity and food security. Anticipated outcomes include higher crop yields and a reduced vulnerability of crops to diseases, contributing to a more sustainable and prosperous agricultural sector.

Internet of things driven multilinear regression technique for fertilizer recommendation for precision agriculture

Food instability has been linked to infertility, health issues, accelerated aging, incorrect insulin regulation, and more. Innovative approaches increased food availability and quality. Agriculture environment monitoring systems need IoT and machine learning. IoT sensors provide all necessary data for agriculture production forecast, fertilizer management, smart irrigation, crop monitoring, crop disease diagnosis, and pest control. Precision agriculture may boost crop yields by prescribing the right water-fertilizer-paste ratio. This article presents IOT based fertilizer recommendation system for Smart agriculture. This framework uses IoT devices and sensors to acquire agriculture-related data, and then machine learning is applied to suggest fertilizer in the correct quantity and at the appropriate time. The data acquisition phase collects input data, including soil temperature, moisture, humidity, regions' weather data, and crop details. Features are selected using the Sequential Forward Floating Selection algorithm. Multilinear Regression performs data classification. The performance of SFSS-MLR is compared to Random Forest, C4.5, Naïve Bayes algorithm. SFSS MLR is better in accuracy, precision, recall and F1. The accuracy of SFSS MLR is 99.3 percent.

SIFAT FISIK TANAH DAN PERTUMBUHAN TANAMAN PADI (Oryza sativa L.) SEBAGAI DAMPAK PENGAPLIKASIAN DEKOMPOSER PADA SISTEM REKOMENDASI PEMUPUKAN BERBASIS CITRA KAMERA MULTISPEKTRAL DI SUKAMANDIJAYA, JAWA BARAT

Application of balanced fertilizers and the use of decomposers on land can improve soil health and nutrient availability for plants. Giving recommended doses of fertilization through the use of multispectral camera images lead to more efficient fertilization. This study aims to analyze the impact of the application of decomposers on the implementation of a multispectral camera image-based fertilizer recommendation system on soil physical properties, growth, and crop yield. This research was carried out from November 2021 to April 2022 at the experimental field in Sukamandijaya Village, Ciasem District, Subang Regency, West Java. This study was carried out with 2 treatments, namely land without the decomposers (P1) and with the decomposers treatment (P2) on rice fields of the Mekongga variety. The decomposer used was Petro Gladiator with dose 5 liters/ha (480 ml/plot or 56 ml/m²). Data analysis was carried out by unpaired T test at 5% level to determine the effect of decomposer application on soil physical properties as well as growth and crop yield. The results showed that the application of decomposers to fertilizer recommendation systems based on multispectral camera images can improve soil conditions including improvements in soil bulk density, soil particle density, total pore space, soil moisture content pF 1, 2, and 4.2, available pore water, then soil pH, available-P soil contents, potential-K soil content, as well as rice plant height.

Implication of blanket NPK application on nutrient balance of maize based on soil and tissue diagnosis approaches in the savannas of northern Nigeria

Improper nutrient management reduces the yield and affects the nutrient status of crops. This study aimed to diagnose the nutrients limitation in maize. A three-year multi-location (348 sites) nutrient experiments were conducted in randomized block design to analyse nutrients limitation for maize production under conventional fertilizer recommendation system in Nigeria using DRIS, and to identify soil factors that influence DRIS indices using random forest model. DRIS indices for nutrients were calculated from the results of ear leaf samples collected from the experimental plots. The DRIS indices were summed, and used to cluster plots using k-means cluster algorithm. The results show large differences in average yield between the clusters. The clusters also differed based on frequency with which nutrients are most limiting. B was the most limiting in cluster one and three, Mn in cluster two and K in cluster four. Random forest results show that soil pH, B and Mg had the largest influence on DRIS indices in cluster one. DRIS indices were most influenced by soil N and B in cluster two. To a lesser extent, the soil Fe, K, Mg and S contents also influenced DRIS indices in cluster two. Soil K, B and Zn were the most significant factors influencing the DRIS indices in cluster four. Bulk Density, Fe, Na, ECEC, and organic carbon had a moderate influence on the indices in this cluster. Nutrient limitation in plants can be diagnose using the DRIS. Soil properties have a definite influence on maize nutrient status.

Crop and Fertilizer Recommendation System

Being a source of food, raw resources, and jobs, agriculture is essential to the global economy. However, with a growing population, the demand for food production has increased, making it imperative to improve crop yield and sustainability. Since agriculture is greatly influenced by the surrounding natural conditions, we face many challenges in actual agriculture practices. one of the biggest challenges faced by farmers in determining the right crop and fertilizer to use for optimal yield. Efficient technology can be used to increase yields and reduce possible challenges in this area. One approach is to use machine learning techniques to propose crops and fertilizers to farmers based on their unique needs. In this article, we present a crop and fertilizer recommendation system developed using efficient ML models. We link our model with a web application that allows users to input their data and receive personalized recommendations in multiple regional languages. Our system aims to provide farmers with an easy-to-use tool that can help optimize their crop yield and increase sustainability.

IoT-Driven Artificial Intelligence Technique for Fertilizer Recommendation Model

Smart farming systems emphasize the need for modern technologies like the Internet of Things, Cloud, and Artificial Intelligence (AI) in the agricultural process. The digital transformation accelerates conventional farming practices to increase crop production with quality. Earlier works failed to integrate AI with sensor technology, guiding agricultural practitioners in a successful direction. Therefore, we propose an architectural model with four layers, including sensor, network, service, and application, aid in deploying a smart farming system with limited energy consumption. Moreover, focusing on the application layer, we implement a deep learning approach to build a fertilizer recommendation system that matches the expert's opinion. Finally, the whole system outcomes are presented as a single mobile application for farmers’ ease of use.

Can the advisory system Nutrient Expert® balance productivity, profitability and sustainability for rice production systems in China?

CONTEXTTo avoid excessive chemical-fertilizer application and improve agricultural productivity in rice, a fertilizer recommendation system called Nutrient Expert® (NE) was designed. However, the ability of NE to balance yield, profitability and environmental sustainability in rice production needs to be further evaluated, as it is still difficult for farmers to assess the proper nitrogen (N) application rate. OBJECTIVEThe objective of this study was to demonstrate the advantages of the NE system in balancing yield, profitability and N loss in rice production, and recommend proper N application rates for different cropping seasons of rice. METHODSThis study describes results from field experiments conducted in five main rice cropping provinces from 2017 to 2020 in China, to investigate any advantages of NE compared with local farmers' practice (FP), and to determine the proper N application rates for different cropping seasons of rice. RESULTS AND CONCLUSIONSCompared with FP, NE had 12.1% lower N-fertilizer application, but increased rice grain yield by 4.3% and net profit by 7.4%, and decreased yield-scaled N loss by 20.7%. We showed how yield, profitability and N loss were affected by N balance (i.e., N applied to the field minus N removed from the field by the harvested crop biomass), and quantified relationships between N balance and N application rate, and between N output and input. Based on relationships between N balance and N application rate, we recommend N application rates in a range from 122 to 214 kg ha−1, depending on cropping seasons of rice. We demonstrated that NE can simultaneously improve yield, profitability and environmental sustainability. SIGNIFICANCEOur study provided quantitative support for NE-based recommendations on the N application rate for smallholders farming in different rice cropping systems, and these recommendations can serve as a reference for avoiding excessive N application rate in paddy fields in other regions with similar eco-environment.

Intelligent insecticide and fertilizer recommendation system based on TPF-CNN for smart farming

Nowadays, artificial intelligence and sensor technology play a vital role in the agriculture field. The use of excess insecticides and fertilizers in farming poses a risk to human health. It is necessary to control them to ensure healthy crop production. Many techniques are used to identify the pest, suggest medications, and do soil nutrient analysis techniques separately. This paper applies the dual operator, Transition Probability Function (TPF), and Convolution Neural Network (CNN) to process the pest's image discretely and continuously for applying the recommended insecticide. The mathematical model with the objective function is derived in this paper. The proposed system combines two major aspects in farming: pest identification and insecticide recommendation using machine vision and CNN. Secondly, the soil nutrient analysis uses a soil NPK sensor with the recommendation of fertilizers according to the obtained nutrient values. On-spot results are obtained, and the time required for insecticide recommendation is within 10 s, and for fertilizer recommendation, it is within 80 s. Successful identification of five pests, namely aphids, bollworms, leaf folder, leaf miner, and green stink bug, was done with more than 90% accuracy. The proposed approach is also compared with the other intelligent approaches, such as Artificial Neural Network (ANN), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM), and it is observed that the proposed TPF-CNN approach gives higher accuracy in the shortest time.

Web Based Leaf Disease Prediction in Crops and Fertilizer Recommendation System Using Deep Learning Technique

Agriculture is the considered as the back bone of India. Agriculture is the most important sector in today’s life. Many plants are affected by various diseases which naturally drops the yield of the crop production in turn farmers are seriously affected. A web-based tool Flask is used to create an application for routing the web pages and also the designing part involves HTML, CSS and static pages. The Data set contains images of the diseased plants of both vegetables and fruits. These images are trained and tested using Deep learning Model building and the appropriate model is created and saved. This saved model is interlinked with the web page for Prediction and recommendation system. An automated system is introduced in the form of identifying different diseases on plants by checking the symptoms shown on the leaves of the plant. Deep learning techniques are used to identify the diseases and suggest the precautions that can be taken for those diseases. To make the system user friendly a User Interface is created for easy access and usage by the farmers. This application will be very useful for farmers.

Prediction of Crop, Fertilizer and Disease Detection for Precision Agriculture

Abstract: The main walk of life of our Country is Agriculture. More than 70 % of the population’s lives depend upon agriculture. It is also a great source of country’s economy. In order to make this filed more profitable for farmers proper crops have to be grown in their fields. The prevalent problem among the farmers is Crop choice depending upon the soil in their farmlands. Another challenge faced by farmers is choosing the right fertilisers for their crops, which plays a very important role in getting a good and profitable yield. There is another major problem which they have to give more attention is the pest control or the diseases to which the plants may limit their growth. The above listed problems may solved using the advanced techniques of Precision Agriculture and data mining. Precision Agriculture is modern technique which can be used for farming. The main objective of is to solve above problems using data mining techniques and build a decision system which would help farmers to choose right crops for their farm , fertiliser recommendation for the crops grown and also to help the farmers in detecting the diseases by using the infected leaf images.. Keywords: Precision Agriculture, Data Mining, Crop, fertilizer recommendation system, ML Algorithms

A Machine Learning Based Crop and Fertilizer Recommendation System

A Machine Learning Based Crop and Fertilizer Recommendation System

Designing an Intelligent Fertilizer Recommendation System for Small-scale Farmers

The agriculture sector is a vital part of the world's population, as it provides food for everyone. Unfortunately, small-scale farmers often face challenges when it comes to choosing the appropriate amount and type of fertilizer to their crops. This paper proposes an intelligent fertilizer recommendation system (IFRS) that uses machine learning techniques. IFRS is designed to help small-scale farmers make informed decisions when it comes to applying fertilizer. It uses machine learning techniques to analyze and recommend the most effective fertilizer for their crops. This system is built on historical data about the various factors that affect the development and maintenance of a farm's soil and crop yield. It uses machine learning techniques to make informed decisions and improve the efficiency of its operations. The system's dashboard is designed to help farmers easily access and interpret the recommendations it provides. It allows them to input their crop characteristics and location, and it provides personalized fertilizer suggestions based on these data. In addition, it displays the weather conditions and other factors that can affect the application of nutrients. The proposed IFRS would provide small-scale farmers with a reliable and efficient fertilizer recommendation system, which would enhance their income and productivity. The system's user-friendly dashboard and accuracy would make it ideal for use in different countries with varying climates and soil types.

Modeling the contributions of nitrogen mineralization to yield of corn

AbstractNitrogen (N) mineralized from soil organic matter (SOM) and crop residues is an important source of N to crop nutrition but historically has been difficult to account for in N fertilizer recommendation systems. Here we propose and test relatively simple biogeochemical models that predict the contribution of N mineralization in supporting the yield of corn (Zea mays L.). The models are specifically designed to use soil and cover crop measurements that are easily accessible to farmers and agronomists, including soil carbon (C) concentration, 24‐h CO2 respiration of a dried and rewetted soil sample, soil texture, and cover crop biomass N content and C/N ratio. We calibrated the models to explain variation of and predict unfertilized corn yield using a dataset of 73 observations compiled from nine experiments conducted in different sites and growing seasons in Pennsylvania. A model using soil C to calculate contributions of SOM mineralization to N supply predicted unfertilized corn yields more accurately than a model using the 24‐h CO2 respiration (r2 = .62 vs. .47; RMSE = 1.58 vs. 1.87 Mg ha−1, respectively). Soil sand content played an important role in the models by regulating the humification efficiency, a term that partitions decomposing C and N between microbially assimilated and mineralized pools. These models are prototypes for a new generation of N decision support tools that answer many of the shortcomings of current N fertilizer recommendation systems and offer a novel step forward in N fertility management.