Agriculture plays a key role in the Indian economy. There are various challenges in the agricultural sector posed by climatic conditions, loss of biodiversity, soil erosion, traditional farming working with plants, diseases, and pests. The impact of using conventional methods of agriculture on the environment include deforestation, soil erosion and depletion of nutrients in the soil. Indian farmers without having any prior knowledge about the atmospheric conditions cannot choose their crops effectively. Incorporating modern agriculture can provide many solutions to these kinds of problems. Precision farming techniques can solve this issue by gathering and evaluating data on temperature, rainfall, soil, seed, crop production, humidity, and wind speed, which will assist farmers in raising agriculture production. The data is refined before being examined and processed using the map reduce architecture. Applying the K-means clustering method to the map reduce results yields a mean accuracy result for the data. Taking Consideration of regions, the relationship between different factors is analyzed using bar graphs and scatter plots which is hugely beneficial for crop prediction. This study gives an analysis of different crop prediction techniques.

A Study on Weather based Crop Prediction System using Big Data Analytics and Machine Learning

This paper presents a study of Indian rainfall and prediction of the annual rainfall in the state of Maharashtra and Konkan. The decreasing trends in seasonal rainfall and post-monsoon rainfall and increasing occurrence of the deficit rainfall years indicates the probable intensification of water scarcity in many states and sub divisions of India. Rainfall serves a major source of water only when it is conserved, thus a proper analysis and estimation of rainfall globally is of utmost importance. In an agricultural country like India, where the majority of agriculture is rain dependent rainfall prediction can help to understand the uncertainty in rainfall pattern which may affect the overall agricultural produce. The present study consists a descriptive analysis of annual rainfall in India from 1950–2020, this visualization may prove helpful for deciding the right model for prediction. This study is aimed at finding the most apt model for making accurate prediction for the rainfall dataset. Two machine learning model and a neural network model are implemented and their results are compared. The performance of the results was measured with MSE (mean squared error), RMSE (root mean square error), MAE (mean absolute error). The machine learning models showed high level of deviation as the time series data in use was highly inconsistent, on the other hand the neural network showed better efficiency due to the local dependency in the model which helps it to learn and perform better. 

Annual Rainfall Prediction of Maharashtra State Using Multiple Regression

Agriculture is the foremost factor which is important for the survival of human beings. Farming contributes to a very big part of GDP; still, several areas exist where improvements are required. One of those is crop recommendation. Crop productivity is boosted as a result of accurate crop prediction. As crop production has already started to suffer from climate change, improving crop output is consequently desirable because agronomists are impotent to select the appropriate crop(s) depending on environmental and soil parameters, and the mechanism of forecasting the selection of the appropriate crops manually has failed. Factors like soil characteristics, soil types, climate characteristics, temperature, rainfall, area, humidity, geographic location etc. affect crop forecast. This chapter focuses mainly on building a recommendation system, i.e., suggesting the kind of the crop by applying various machine learning and deep learning techniques depending upon several parameters. The system would help the farmers for the appropriate decision to be taken regarding the crop type. 

AI-Enabled Crop Recommendation System Based on Soil and Weather Patterns

Agriculture plays a key role in the Indian economy. There are various challenges in the agricultural sector posed by climatic conditions, loss of biodiversity, soil erosion, traditional farming working with plants, diseases, and pests. The impact of using conventional methods of agriculture on the environment include deforestation, soil erosion and depletion of nutrients in the soil. Indian farmers without having any prior knowledge about the atmospheric conditions cannot choose their crops effectively. Incorporating modern agriculture can provide many solutions to these kinds of problems. Precision farming techniques can solve this issue by gathering and evaluating data on temperature, rainfall, soil, seed, crop production, humidity, and wind speed, which will assist farmers in raising agriculture production. The data is refined before being examined and processed using the map reduce architecture. Applying the K-means clustering method to the map reduce results yields a mean accuracy result for the data. Taking Consideration of regions, the relationship between different factors is analyzed using bar graphs and scatter plots which is hugely beneficial for crop prediction. This study gives an analysis of different crop prediction techniques. 

In India 58% of the population depend upon agriculture and agriculture contributes about 18% of the gross domestic product. But the farmers face numerous challenges in agriculture due to various reasons. The farmers’ crop selection criteria generally depended upon their traditional base and testimonials of the success stories. The failure of selecting appropriate crop for a particular area makes them vulnerable to other vagaries also. Scientifically crop selection depends upon various factors like soil parameter (Ex: Soil moisture), weather parameters (Ex: Rainfall, temperature, Humidity), Geographical parameters (Ex: Slope) etc. Farmers and those who are venturing into farming requires a crop prediction model or a technique for the best crop selection decision in order to cope up and tide over exigencies like climate change, flood, drought etc which occur globally. The model will be accurate to tackle the issues arised by the climate change and also gives reasonable results to the farmers. This paper review on the existing crop prediction model and also their implication on the farming community.

Crop Prediction Methods: A Comparative Study

This paper aims at collecting and analysing temperature, rainfall, soil, seed, crop production, humidity and wind speed data (in a few regions), which will help the farmers improve the produce of their crops. Firstly, we pre-process the data in a Python environment and then apply the MapReduce framework, which further analyses and processes the large volume of data. Secondly, k-means clustering is employed on results gained from MapReduce and provides a mean result on the data in terms of accuracy. After that, we use bar graphs and scatter plots to study the relationship between the crop, rainfall, temperature, soil and seed type of two regions (Ahmednagar, Maharashtra and, Andaman and Nicobar Islands). Further, a self-designed recommender system has been used to predict the crops and display them on a Graphic User Interface designed in a Flask environment. The system design is scalable and can be used to find the recommended crops of other states in a similar manner in the future.

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Oorja Garg

Papers

WB-CPI: Weather Based Crop Prediction in India Using Big Data Analytics