Automatic AgriAdvice Generator Using Soil Health Card and Crop Calender for Precision Essay

Automatic Agri-Advice Generator Using Soil Health Card and Crop Calender for Precision Farming Priti.D.ChavanDepartment of ComputerEngineeringPimpri Chinchwad College ofEngineeringPune, [email protected] of ComputerEngineeringPimpri Chinchwad College ofEngineeringPune, [email protected] of ComputerEngineeringPimpri Chinchwad College ofEngineeringPune, [email protected] Prof.S.R.VisputeDepartment of Computer EngineeringPimpri Chinchwad College of Engineering Pune, [email protected] Abstract” Today energy resources have become scarcer and so additional valuable. In conjunction with the growth over the last century, the necessity for locating new, additional economical, and property ways of agricultural cultivation and food production has become additional crucial.

To facilitate this method, we are designing, building, and evaluating a system for exactness agriculture that provides farmers with helpful information regarding the crop prediction based on NPK values of soil, the water offer, and additionally, the general data of the diseases in a user_friendly, simply accessible manner. Our system aims to create cultivation and irrigation more economical as a result of the farmer is ready to make higher informed decisions and so save time and resources.

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The variety of location and environmental condition effects upon agricultural cultivation, along with other environmental parameters over time makes the farmer’s call making method more sophisticated and needs additional empirical information. Applying wireless sensing element networks for observation weather parameters and combining this data with a user-customized service could modify farmers to use their knowledge in associate degree economical manner so as to extract the simplest results from their agricultural cultivation. The system will scale supported every farmer’s demands and additionally the resulting ensemble of collected data could represent a valuable resource for future use, additionally to its use for real-time decision creating. The look of the precision agriculture system contains a model resolution concerning the sensing element platform and a customizable service that may be used in numerousKeywords”K-Nearest-Neighbour, Farmland, NPK sensor.I. INTRODUCTIONAs the world is trending into new technologies and implementations it’s a necessary goal to trend up agriculture additionally. Several research has been wiped out the sector of agriculture. Most of comes signify the use of wireless sensor network collect knowledge from completely different sensors deployed at varied nodes and send it through the wireless protocol. The collected knowledge gives knowledge regarding the varied environmental factors such as NPK values of soil etc. Monitoring the environmental factors isn’t the whole answer to extend the yield of crops. There is range of alternative factors that decrease productivity to a larger extent. In Republic of India around eightieth of individuals rely on farming. Sensible Agriculture is one amongst the solutions to the current downside. To highlight options of this project includes NPK value of soil, water level, forecasting, canal dominant in each automatic associate degreed manual modes and each one these knowledge is hold on and displayed in an exceedingly mobile application. By dominant of these operations by a mobile that is connected to the net and it’ll give higher performed by interfacing sensors, native area network, etc. consists of Machine Learning algorithm for crop prediction based on NPK value..II. PROPOSED SYSTEMThis project is implemented using Arduino Micro-controller. Here we tend for using Hardware like moisture sensor and Motor On and off switch. Here we have dynamically monitored agriculture parameters using IoT. The projected system consists of the Soil moisture sensor, Temperature sensor, fire detector, humidity sensor and NPK detector, Bluetooth module and Field phone to store the information received from the farm. All the sensors are interfaced to Arduino. This system monitors and records the values of temperature, soil moisture, Fire and moisturize level of the soil of the natural surroundings that are continuously updated so as to optimize them to attain most plant growth and prevention from disease. All this information is distributed via Bluetooth module to the field mobile where we have designed android APP to monitor the parameters. This field mobile acts as a server. The user will continuously monitor and control the parameters as per his need. The Sensors are mounted on plywood where NPK detector is mounted inside the soil and held below the ground level. The NPK sensor will check nitrogen, phosphorous and potassium quantity within the soil and based on that the system can predict which crop is useful. The moisture device is held which is able to sense the moisture level and switch the motor on and off consequently. Firing device is used to sense the fire existence within the field if the fire gets exist the buzzer will continuously make noisy. The disease prediction will be analyzed based on analysis of the parameters comes from the sensor as a result.III. SYSTEM DESIGN IV. ADVANTAGES It protects the crops by notifying the farmer regarding weather changes and the probabilities of disease attacks. The system can facilitate the farmer to get information on fire exist in the field. The system can predict the profitable crop based on NPK Water Conservation “Weather predictions and soil moisture sensors allow for water use only when needed. Remote Monitoring “Local and commercial farmers will monitor multiple fields in multiple locations around the globe from a web connection. decisions are made in real-time and from anywhere.V. ALGORITHM USEDA.DefinitionK nearest neighbors is simplest algorithm which stores all functonal data points and classifies a new sample based on a similarity measure (eg.Euclidean distance functions). Its a non-parametric algorithm used to predict the classification of the new sample point. Classification is done by a majority vote to its neighbors. The data is assigned to the class which has the most closest neighbors. As you increase the number of nearest neighbors, the value of k, accuracy might increaseB.Algorithm steps Let m be the number of training data samples. Let p be an unknown point. 1. Store the training samples in an array of data points arr[], this means each element of this array represents a tuple (x, y). 2. for i=0 to m: Calculate Euclidean distance d(arr[i], p). 3. Make set S of K smallest distances obtained. Each of these distances correspond to an already classified data point. Return the majority label among S.C.Pseudo Code1. Load the training and test data 2. Choose the value of K 3. For each point in test data: – find the Euclidean distance to all training data points – store the Euclidean distances in a list and sort it – choose the first k points – assign a class to the test point based on the majority of classes present in the chosen points4. EndD.Mathematical Model S2={s, e, X, Y, F} Where,s = Initial State: Input data set without classificatione = End State: Classified datasetX = Input to the system. Here it is training and testing data set in any suitable file format such as XLS, CSV, ARFF, class attribute with the defined class Y = Output. Classified dataset as per defined class F = Algorithm/Function used in the program. E. Algorithmic AnalysisWe have successfully completed the comparative analysis of various algorithms and based on that we have used the KNN(K-Nearest-Neighbors) algorithm for crop prediction using NPK values of soil. In Algorithmic Analysis we got KNN algorithm Accuracy as 91.8% i.e highest accuracy, So we have implemented a KNN algorithm in or project.VI. HARDWARE VII. DATASET AND RESULTSThe dataset contains soil attributes including macronutrients (N, P, K)A.datasetTable 1: Labelled Dataset(crop )Id Name nitrogen phosperous potassium1 Rice 10 20 02 Wheat 12 26 183 Maize 18 0 144 Sugarcane 20 10 55 Potato 8 18 266 Mustured 100 30 157 Jowar 120 60 08 Cotton 15 15 159 G-Hisrsutum 75 30 010 Groundnut 15 15 1511 Onion 15 15 1512 Banana 15 15 1513 Tomato 15 15 1514 Leafy Veg 15 15 1515 Redgram 4 2 1Table 2: Labelled Dataset(crop calendar)id crop season from to period1 Maize Kharif June(Beg) Dec(Beg) sowing2 Maize Rabi Jan(Beg) Jan (Beg) Harvesting 3 Wheat Rabi Oct (Beg) Dec (End) Sowing4 Wheat Rabi Feb (Beg) March(End) Harvesting5 Rice kharif May(Beg) Nov (Mid) Sowing 6 Rice Rabi Dec(Beg) Jan (Beg) Harvesting7 Redgram Kharif June(Beg) Dec(Beg) Sowing 8 Redgram Kharif July(Beg) July (End) Sowing 9 Sugarcane Rabi Nov(Beg) Dec(End) Sowing 19 Sugarcane Rabi Oct(Beg) April(End) HarvestingB. Results We got the crop suitable for the soil depending on NPK values and crop calendar. The period is also associated with it for sowing or harvesting of the crop. There are Various functionalities like the Fire sensor is alerted when fire is caught, Temperature and Humidity sensors for providing precautions and solutions for the diseases of different crops. VII. CONCLUSIONIn the propose, a completely unique System Enabled: IoT based mostly on Live observance Soil moisture has been planned using Arduino. The sensors have high potency and accuracy in attractive the live data of soil moisture. The system allows effective soil, water, moisture, parameters have been observance and change using IOT. This permits effective crop prediction based on NPK value, soil maintenance, and disease prevention mechanism. This overcomes the manual operations needed to watch and maintain the agricultural farms. The system allows the farmer to search regarding the various maladies. Our aim is to develop a farmer-friendly agricultural system. Earlier, the farmer had to keep tons of vigil on his fields however with this project the time wasted in monitoring the fields has been reduced with the assistance of sensors and alert systems that have been enforced hence, the project has been created keeping both the farmer’s yet as the environment in mind.VIII. ACKNOWLEDGMENTThe authors wish to express and acknowledge sincere thanks to Dr. J. S. Umale Sir and our seminar guide Prof. S. R. Vispute Madam for support and guidance for the useful suggestions on different topics on multiple occasions.IX. REFERENCES[1] J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 3rd ed., 2011[2] G.Nasrin Fathima and R.Geetha, Agriculture Crop Pattern Using Data Mining Techniques., International Journal of Advanced Research in Computer Science and Software Engineering, 2014[3] S.Pudumalar , E.Ramanujam , R.Harine Rajashreen , C.Kavyan , T.Kiruthikan , J.Nishan , Crop Recommendation System for Precision Agriculture,IEEE Eighth International Conference on Advanced Computing (IcoAC) , 2016[4] Geraldin B. Dela Cruz, Bobby D. Gerardo, Bartolome T. Tanguilig , Agricultural Crops Classification Models Based on PCA-GA Implementation in Data Mining, International Journal of Modeling and Optimization, Vol. 4, No. 5, October 2014[5] KM Archana Patel and Prateek Thakral , The Best Clustering Algorithms in Data Mining, International Conference on Communication and Signal Processing, April 6-8, 2016[6] M.C.S.Geetha , Implementation of Association Rule Mining for different soil types in Agriculture ,International Journal of Advanced Research in Computer and Communication EngineeringVol. 4, Issue 4, April 2015[7] Ruowu Zhong and Huiping Wang, Research of Commonly Used Association Rules Mining Algorithm in Data Mining, International Conference on Internet Computing and Information Services,2011[8] Karan Kansara, Vishal Zaveri, Shreyans Shah, Sandip Delwadkar, Kaushal Jani , Sensor based Automated Irrigation System with IOT , International Journal of Computer Science and Information Technologies, Vol. 6(6) , 2015, 5331-5333.[9] Umid Kumar Dey, Abdullah Hasan Masud,Mohammed Nazim Uddin,Rice Yield Prediction Model Using Data Mining,International Conference on Electrical, Computer and Communication Engineering (ECCE), February 16-18, 2017.[10] P. Vinciya ,Dr. A. Valarmathi ,Agriculture Analysis for next Generation High Tech farming in Data Mining.,International Journal of advanced research in computer science and software engineering,2016.[11] Abishek.B ,R.Priyatharshini, Akash Eswar M,P.Deepika, Prediction of Effective Rainfall and Crop Water Needs using Data Mining Techniques,2017 IEEE International Conference on Technological Innovations in ICT For Agriculture and Rural Development(TIAR 2017).[12] Jessica Maikhanh Brown Clarksburg High School, Clarksburg Maryland, jessica, Predicting Math Test Scores Using K-Nearest Neighbor , IEEE Integrated STEM Conference (ISEC), 2017[13] S.Sivaranjani and Dr.S.Sivakumari , Crime Prediction and Forecasting in Tamilnadu using Clustering Approaches , International Conference on Emerging Technological Trends [ICETT] , 2016[14] Min Chen, Yixue Hao, Kai Hwang, Fellow, IEEE, Lu Wang, and Lin Wang , Disease Prediction by Machine Learning over Big Data from Healthcare Communities , Accepted in journal[15] S.Nagini and Dr. T. V. Rajini Kanth,Agriculture Yield Prediction Using Predictive Analytic Techniques, 2nd International Conference on Contemporary Computing and Informatics (ic3i),2016[16] Wu Fan 1 , Chen Chong 2 , Guo Xiaoling 2 , Yu Hua * and Wang Juyun 2 School of Science,Prediction of crop yield using big data,8th International Symposium on Computational Intelligence and Design,2015[17] R.Sujatha and Dr.P.Isakki @ Devi Assistant Professor , A Study on Crop Yield Forecasting Using Classification Techniques [18] Vinit Sawant, S. R. Vispute, A Survey of Farmer’s need and Feedback Analysis System, IJIRCCE 2015,[19] Nikita Shahane, S. R. Vispute, Survey of Big Data Analysis Using Predictive Analytics Algorithms and Its Use Cases, IJICAR-2015[20] Sushma Rahul Vispute, S. Kanthekar, A.kadam, C.Kunte, P Kadam, Automatic Personalized Marathi Content Generation, IEEE International conference on CSCITA-2014. [21] Sushma R. Vispute, Shrikant Patil, Sagar Sangale, Akshay Padwal, Avinash Ukarde, Parallel Processing System for Marathi Content Generation, International Conference on Computing Communication Control and Automation,2015.[22] Aarti Kalekar, Prof. Sushma Vispute, Priti Kokane, Manali Kamble, Kajal Bokefode, Automated Generation and Analysis of Soil Health Card and Calculation of the Village Soil Fertility Index,International Journal of New Technologies in Science and Engineering Vol. 3, Issue. 1, 2016.

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