MACHINE LEARNING CONVERGENCE FOR WEATHER BASED CROP SELECTION AND PROCESSING FOR SMART AGRICULTURE

2020 ◽  
Vol 14 (11) ◽  
Keyword(s):  
Machine Learning ◽  
Crop Selection ◽  
Learning Convergence ◽  
Smart Agriculture

Digital Technologies for Smart Agriculture

2021 ◽  
pp. 54-67
Author(s):  
Garima Singh ◽  
Gurjit Kaur
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Digital Technologies ◽  
Agricultural Crop ◽  
Seed Selection ◽  
Crop Selection ◽  
Soil Preparation ◽  
Crop Cycle ◽  
Smart Agriculture

This chapter will provide the reader with an introduction to the modern emerging technologies like cloud computing, machine learning, and artificial intelligence used in agriculture. Then a glimpse of complete crop cycle follows, including seven steps, namely crop selection, soil preparation, seed selection, seed sowing, irrigation, crop growth, fertilizing and harvesting; and how these digital technologies are helpful for the crop cycle is also explained in this chapter. The rest of the chapter will explain the merger of the modern digital technologies with the agricultural crop cycle and how the future farming will work.

Swarm Intelligence Optimization: An Exploration and Application of Machine Learning Technology

2021 ◽  
Vol 30 (1) ◽  
pp. 460-469
Author(s):  
Yinying Cai ◽  
Amit Sharma
Keyword(s):  
Machine Learning ◽  
Swarm Intelligence ◽  
Research Result ◽  
Machine Learning Algorithms ◽  
Learning Technology ◽  
Data Set ◽  
Rice Pests ◽  
Machine Leaning ◽  
Smart Agriculture ◽  
Swarm Intelligence Optimization

Abstract In the agriculture development and growth, the efficient machinery and equipment plays an important role. Various research studies are involved in the implementation of the research and patents to aid the smart agriculture and authors and reviewers that machine leaning technologies are providing the best support for this growth. To explore machine learning technology and machine learning algorithms, the most of the applications are studied based on the swarm intelligence optimization. An optimized V3CFOA-RF model is built through V3CFOA. The algorithm is tested in the data set collected concerning rice pests, later analyzed and compared in detail with other existing algorithms. The research result shows that the model and algorithm proposed are not only more accurate in recognition and prediction, but also solve the time lagging problem to a degree. The model and algorithm helped realize a higher accuracy in crop pest prediction, which ensures a more stable and higher output of rice. Thus they can be employed as an important decision-making instrument in the agricultural production sector.

scholarly journals Machine Learning Approach for Crop Selection based on Agro-Climatic Conditions

IJARCCE ◽  
2018 ◽  
Vol 7 (10) ◽  
pp. 103-106 ◽  
Author(s):  
Prof.K.D. Yesugade ◽  
Aditi Kharde ◽  
Kajal Muley
Keyword(s):  
Machine Learning ◽  
Climatic Conditions ◽  
Learning Approach ◽  
Crop Selection ◽  
Machine Learning Approach

scholarly journals Machine Learning For Agribusiness using GIS

2019 ◽  
Vol 8 (2) ◽  
pp. 1249-1251
Keyword(s):  
Machine Learning ◽  
Information System ◽  
Geographic Information System ◽  
High Precision ◽  
Geographic Information ◽  
Operational Conditions ◽  
Smart Agriculture

In present days we have discussed about the emerging concept of smart agriculture that makes agriculture more efficient, effective and farmers save money and time with the help of high precision algorithms and Geographic Information System (GIS).The component that drives it is GIS with Machine Learning the logical field that enables machines to learn without being carefully customized. It has developed together with huge information advances and elite registering to make new chances to disentangle, measures, and comprehends information concentrated procedures in farming operational conditions. For instance, ranchers use accuracy GPS on the field spare manure. Ranchers use precision agribusiness since they can lessen the proportion of manure fertilizer. Moreover, satellites and robots assemble vegetation, topography and atmosphere information from the sky. This information can go into developing maps for better fundamental activity.

scholarly journals Crop Yield and Rainfall Prediction in Tumakuru District using Machine Learning

2019 ◽  
Author(s):  
Girish L
Keyword(s):  
Machine Learning ◽  
Call Center ◽  
Market Price ◽  
Predictive Analysis ◽  
Learning Technology ◽  
Rainfall Prediction ◽  
Analysis Technique ◽  
The Future ◽  
The Government ◽  
Smart Agriculture

Smart Agriculture is a development that emphasizes the use of information technology in the farming. Mostof the population in India depending on agriculture. This situation is one of the reason, that hindering the developmentof country. Nowadays, even though farmers get more yield for their crop but the market price for that crop will be less,in that case farmers get loss for their product and vice versa. Particularly, when growing new crops, farmers face therisks of both market price and production problems. To overcome these problems, a machine learning technology isused. Predictive analysis is a branch of data mining which predicts the future probabilities and trends. The predictionwill help the farmers to choose whether the particular crop is suitable for specific rainfall and crop price values. Thisapproach is to increase the net yield rate of the crop, based on rainfall. Prediction can be carried out by using variousmachine learning algorithms like linear regression, SVM, K NN method and decision tree algorithm out of which SVMis giving the highest efficiency. The predictive analysis technique can be implemented in several government sectors likeAPMC, kissan call center etc., by which the government and farmers can get the information of the future rainfall, cropyield and the market price.

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