International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022
p-ISSN: 2395-0072
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A COMPREHENSIVE SURVEY ON AGRICULTURE ADVISORY SYSTEM Pooja K C1, Pooja K P2, Pooja N G3, Dr. A B Rajendra4 Students, Information Science and Engineering Head of Department, Dept. of Information Science and Engineering Vidyavardhaka College of Engineering, Mysuru, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------1-3
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Abstract - Agriculture has a major role in the country's
appropriateness levels based on input and training data using a variety of methods. These machine learning models, as well as the performance of each method, are compared to the hybrid technique. We predict crops and offer the necessary fertilizers to improve agricultural output in the proposed work. For crop production estimation and fertilizer recommendations, we employ a variety of agriculture characteristics. Supervised learning algorithms are used for the recommendations such as either "Bayesian classifier" or "K nearest neighbour" or "Random Forest" algorithm. These algorithms are preferred as they work efficiently, generate faster results, and also work for all formats of data also, few survey papers suggest these algorithms are efficient and good for agriculture data-sets.
social and economic development and progress. Farmers' failure to select the appropriate crop for cultivation is a major source of crop productivity loss. There is currently no mechanism to advise farmers on which crops to plant. Predicting the appropriate crops to produce and suggesting proper fertilizers to improve crop output well before the harvest. The prediction method is appropriate for data science applications since it incorporates a large number of databases. Using data science, we extract insights from vast volumes of data. The system gives a research paper on the various machine learning algorithms for forecasting the best crops and fertilizer recommendations. The accuracy with which features have been extracted also with efficient classifiers being used are the critical factors in any crop prediction system's performance.
2. LITERATURE REVIEW 1. Developing innovative applications in agriculture using data mining was offered by Sally Jo Cunningham and Geoffrey Holmes. The methods involved in this are Weka classifiers: ZeroR, OneR, Naïve Byes, Decision Table, Ibk, J48, SMO, Linear Regression, M5Prime, LWR, Decision Stump and association rule include apriori algorithm and also it includes the EM clustering algorithm for the purpose of clustering. This produces a classifier, which is frequently in the decision tree form or a collection of guidelines that can be used in predicting the categorization of newer data instances. This approach recognises that machine learning technology is still expanding and improving, with learning algorithms that must be delivered to the peoples system who deal with the data and are also familiar with the application domain from which it originates. Weka is a huge step forward in bringing machine learning into the workplace.
Key Words: Data science, GDP, Naïve Bayes algorithm, KNN, Random Forest, GUI, Crop prediction, Data Mining
1. INTRODUCTION Agriculture is India's primary source of income and it is indeed an example of a sector that generates only about 14% of GDP yet has a significant impact on the Indian economy. Better practices are required to increase the yield of the crop as well as the living of farmers as well. Agriculture has evolved as a result of globalization, adopting the latest technologies and practices for a higher level of living. Precision agriculture is one of the newer technologies and approaches in the world of agriculture. Precision agriculture is primarily concerned with farming on a site-by-site basis. Crop and fertilizer recommendations are one of the most important aspects of precision agriculture. Crop suggestion is based on several factors, and precision agriculture technologies aid in detecting these factors, allowing for improved crop selection. A systematic evaluation of enormous volumes of data being gathered from various variables including the parameters like soil quality, temperature, pH, N, urea, P, K, humidity, and so on is required to predict a crop in advance.
2. D Ramesh, and B Vishnu Vardhan detailed Data Mining Techniques and Applications to Agricultural Yield Data. The KNN and K-Means Algorithms are used in this. The system can anticipate the average yield production by examining the cluster to which the forecasted rainfall belongs in this procedure, given the rainfall in a specific year. It also mentions that the K-Means algorithm can split samples into clusters, but no consideration is given to the substances that cause this partition. This type of information can be obtained using bi-clustering.
Understanding the relative contribution of climate elements to agricultural output could give farmers useful knowledge on crop planting and management in the face of climate change. Machine learning approaches can predict
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3. Monali Paul, at el. Described the Analysis of soil Behaviour and Prediction of Crop Yield using the Data Mining Approach
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