Machine Learning Technique for Crop Recommendation in Agriculture Sector

Abstract

Numerous efforts have been demonstrated through various innovations to lead towards the betterment of agriculture sector till now. Effective and innovative use of Science and Technology can help to improve crop quality and production, yield prediction and crop disease analysis. Agriculture sector provides various productions such as food, raw material for industry and has a significant impact on economy and employment of a country. The agriculture sector contains huge data with respect to factors affecting its input and output. With advances in technology, various data mining techniques have been introduced accordingly. Also different analytical models like Decision Tree, Random Forest, Support Vector Machine and Bayesian Neural Network are available for required analysis. These data mining techniques can be used to analyze the multidimensional, time specific data of agriculture sector to produce effective knowledge in support of the efforts to boost the economy. These methods can be further used to analyze soil, climate, moisture, humidity, temperature. In this paper, we used Naive Bayesian Classification technique to identify the class of crop and used the food grain dataset to analyze the technique over different attribute. Further we used Machine Learning approach for the accurate crop recommendation. Crop recommendation will help for effective decision making to the end user to take proper decision according to the output of the system.

Técnicas de Minería de datos aplicados a la agricultura: Estado del Arte y análisis bibliométrico

“…[32], [3]. Predicción de lluvia [24] Agglomotive Hierarchical Clustering Region based routing [6] Equalized cluster head election routing protocol [6] Terrain based Routing using Fuzzy rules for precision agriculture" [6], [53] Segmentación [48], [40] Segmento de tiempo [73] Clasificación de Bayes ingenuo con distribución gaussiana [46] Reglas de asociación [46], [3] Descomposición de abono verde y Redes de reglas de asociación [43] Recomendación cultivo caña de azúcar [60] Minería de regresión multivariable) [46] Ensemble learning [9] Red neuronal recurrente espaciotemporal (STRNN) [9] Deep learning Toma de decisiones de datos agrícolas [56] Rendimiento de cultivo -QRECF-DFFMPC [59] Estudio del crecimiento de la col [26] Sistemas de recomendación Recomendación cultivo caña de azúcar [60] K-NN [74], [37], [78] , [3], Calidad de agua [22] Predicción del nivel de humedad [38] Naive bayes ( nb) [74], [78] Mejorar la calidad de los cultivos [58] clasificación de hongos comestibles o no comestibles [70] Rendimiento del cultivo de arroz [29] Bayes net clasificación de hongos comestibles o no comestibles [70] [80] ZeroR clasificación de hongos comestibles o no comestibles [70] K-measns clustering [33] Bagged-C4.5 [79] Regresión lineal [72], Calidad de agua [22] ARIMA (Autoregressive Integrated Moving Average) [81] Bagging (BG) …”

An Acquisition Based Optimised Crop Recommendation System with Machine Learning Algorithm

“…After that, the moths arrange themselves differently in relation to the appropriate flames. The quantity of flames to be followed reduced as the number of iterations raised, as in equation (3). N flames = round (N − l * (N − 1) / T)…”

Machine Learning for Detection and Prediction of Crop Diseases and Pests: A Comprehensive Survey

“…A model developed by Diepeveen et al in [48] can be used in agriculture to understand the influence of location and temperature on crops. In addition, elements such as soil, humidity, rainfall, and moisture were found to have an influence on crop yield [49].…”