scholarly journals Crop Recommendation System to Maximize Crop Yield in Ramtek region using Machine Learning

2019 ◽  
pp. 485-489
Author(s):  
D. Anantha Reddy ◽  
Bhagyashri Dadore ◽  
Aarti Watekar
Keyword(s):  
Crop Yield ◽  
Precision Agriculture ◽  
Recommendation System ◽  
Statistical Data ◽  
Weather Condition ◽  
Ground Truth ◽  
Majority Voting ◽  
Soil Parameters ◽  
Yield Data ◽  
Yield State

In Indian economy and employment agriculture plays major role. The most common problem faced by the Indian farmers is they do not opt crop based on the necessity of soil, as a result they face serious setback in productivity. This problem can be addressed through precision agriculture. This method takes three parameters into consideration, viz: soil characteristics, soil types and crop yield data collection based on these parameters suggesting the farmer suitable crop to be cultivated. Precision agriculture helps in reduction of non suitable crop which indeed increases productivity, apart from the following advantages like efficacy in input as well as output and better decision making for farming. This method gives solutions like proposing a recommendation system through an ensemble model with majority voting techniques using random tree, CHAID, K _ Nearest Neighbour and Naive Bayes as learner to recommend suitable crop based on soil parameters with high specific accuracy and efficiency. The classified image generated by these techniques consists of ground truth statistical data and parameters of it are weather, crop yield, state and district wise crops to predict the yield of a particular crop under particular weather condition.

scholarly journals Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 646
Author(s):  
Bini Darwin ◽  
Pamela Dharmaraj ◽  
Shajin Prince ◽  
Daniela Elena Popescu ◽  
Duraisamy Jude Hemanth
Keyword(s):  
Deep Learning ◽  
Crop Yield ◽  
Precision Agriculture ◽  
Soil Parameters ◽  
Learning Models ◽  
Weed Detection ◽  
Agricultural Industry ◽  
Agricultural Robots ◽  
Detection Techniques ◽  
Learning Techniques

Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic.

scholarly journals A Mathematical Model to Estimate Soil Parameters using Wireless Sensor: An Efficient Way of Data Collection in Precision Agriculture

2019 ◽  
Vol 9 (2) ◽  
pp. 363-372
Keyword(s):  
Mathematical Model ◽  
Soil Moisture ◽  
Data Collection ◽  
Soil Ph ◽  
Precision Agriculture ◽  
Agricultural Productivity ◽  
Ground Truth ◽  
Wireless Sensor ◽  
Real Field ◽  
Soil Parameters

Environmental and field parameters have high impact on agricultural productivity. The environmental parameters include temperature, humidity, rainfall and wind direction etc. whereas field parameters include salinity, nutrient content, oxygen levels, soil type, soil PH and soil moisture etc. Among them, Soil pH and moisture are highly correlated which can affect soil salinity, nutrient level and soil conductivity. So, these two parameters need to be measure precisely to take management decisions. Till now the process which is applied to measure soil parameters are entirely depends on laboratory testing of soil sample. This process has some overhead like availability of laboratory, manpower and cost. To overcome these challenges, we developed a virgin sensor based automated data collection technique which maintains the agricultural productivity with sustainable development. But practically sensor-based data retain some error which defers from laboratory result. In this paper, we have developed a new efficient technique to reduce the error in calculating pH value using sensors. In this research work, ten different types of soils are tested in laboratory to get the actual (pH level in soil. These values are considered as the ground truth for the experiment. The pH values of all the ten types of soil are collected from the field using wireless sensor. Our proposed mathematical model reduces the error to 0.01 between collected values and ground truth values with back propagation method based on soil moisture, environmental temperature and humidity. Here, the proposed model is empirically tested by taking some real field data values.

scholarly journals The Precision Agriculture Using Artificial Intelligence

2021 ◽  
Vol 9 (11) ◽  
pp. 342-345
Author(s):  
Dr. C. K. Gomathy
Keyword(s):  
Artificial Intelligence ◽  
Crop Yield ◽  
Precision Agriculture ◽  
Recurrent Neural Networks ◽  
Soil Management ◽  
Data Driven ◽  
Soil Parameters ◽  
Analysis Model ◽  
Hybrid Neural Network ◽  
Technological Developments

Abstract: Agriculture has been the sector of paramount importance as it feeds the country's population along with contributing to the GDP. Crop yield varies with a combination of factors including soil properties, climate, elevation and irrigation technique. Technological developments have fallen short in estimating the yield based on this joint dependence of the said factors. Hence, in this project a data-driven model that learns by historic soil as well as rainfall data to analyse and predict crop yield over seasons in several districts, has been developed. For this study, a particular crop, Rice, is considered. The designed hybrid neural network model identifies optimal combinations of soil parameters and blends it with the rainfall pattern in a selected region to evolve the expected crop yield. The backbone for the predictive analysis model with respect to the rainfall is based on the TimeSeries approach in Supervised Learning. The technology used for the final prediction of the crop yield is again a branch of Machine Learning, known as Recurrent Neural Networks. With two inter-communicating data-driven models working at the backend, the final predictions obtained were successful in depicting the interdependence between soil parameters for yield and weather attributes. Keywords: Precision agriculture, Artificial intelligence, Crop management, Solutions, Yield, Soil management

Advances in precision agriculture in south-eastern Australia. II. Spatio-temporal prediction of crop yield using terrain derivatives and proximally sensed data

2009 ◽  
Vol 60 (9) ◽  
pp. 859 ◽  
Author(s):  
N. J. Robinson ◽  
P. C. Rampant ◽  
A. P. L. Callinan ◽  
M. A. Rab ◽  
P. D. Fisher
Keyword(s):  
Crop Yield ◽  
Precision Agriculture ◽  
Spatial Regression ◽  
Spatial Modelling ◽  
Yield Data ◽  
Temporal Prediction ◽  
Eastern Australia ◽  
Elevation Data ◽  
Γ Ray ◽  
Spatio Temporal

The effects of seasonal as well as spatial variability in yield maps for precision farming are poorly understood, and as a consequence may lead to low predictability of future crop yield. The potential to utilise terrain derivatives and proximally sensed datasets to improve this situation was explored. Yield data for four seasons between 1996 and 2005, proximal datasets including EM38, EM31, and γ-ray spectra for 2003–06, were collected from a site near Birchip. Elevation data were obtained from a Differential Global Positioning System and terrain derivatives were formulated. Yield zones developed from grain yield data and yield biomass estimations were included in this analysis. Statistical analysis methods, including spatial regression modelling, discriminant analysis via canonical variates analysis, and Bayesian spatial modelling, were undertaken to examine predictive capabilities of these datasets. Modelling of proximal data in association with crop yield found that EM38h, EM38v, and γ-ray total count were significantly correlated with yield for all seasons, while the terrain derivatives, relative elevation, slope, and elevation, were associated with yield for one season (1996, 1998, or 2005) only. Terrain derivatives, aspect, and profile and planimetric curvature were not associated with yield. Modest predictions of crop yield were established using these variables for the 1996 yield, while poor predictions were established in modelling yield zones.

Crop Recommendation System for Precision Agriculture

2019 ◽  
Vol 7 (5) ◽  
pp. 1277-1282
Author(s):  
Bharath Kumar R ◽  
Balakrishna K ◽  
Bency Celso A ◽  
Siddesha M ◽  
Sushmitha R
Keyword(s):  
Precision Agriculture ◽  
Recommendation System

scholarly journals Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

2019 ◽  
Vol 11 (10) ◽  
pp. 1157 ◽  
Author(s):  
Jorge Fuentes-Pacheco ◽  
Juan Torres-Olivares ◽  
Edgar Roman-Rangel ◽  
Salvador Cervantes ◽  
Porfirio Juarez-Lopez ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Image Data ◽  
Ground Truth ◽  
Aerial Images ◽  
Aerial Image ◽  
Data Set ◽  
Visual Appearance ◽  
Aerial Robots ◽  
Lighting Conditions ◽  
Convolutional Encoder

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

scholarly journals The Use of Crop Yield Autocorrelation Data as a Sustainable Approach to Adjust Agronomic Inputs

2021 ◽  
Vol 13 (4) ◽  
pp. 2362
Author(s):  
Thomas M. Koutsos ◽  
Georgios C. Menexes ◽  
Andreas P. Mamolos
Keyword(s):  
Decision Making ◽  
Spatial Analysis ◽  
Crop Yield ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Yield Data ◽  
Site Specific ◽  
Production Areas ◽  
Short Time ◽  
Monitor Data

Agricultural fields have natural within-field soil variations that can be extensive, are usually contiguous, and are not always traceable. As a result, in many cases, site-specific attention is required to adjust inputs and optimize crop performance. Researchers, such as agronomists, agricultural engineers, or economists and other scientists, have shown increased interest in performing yield monitor data analysis to improve farmers’ decision-making concerning the better management of the agronomic inputs in the fields, while following a much more sustainable approach. In this case, spatial analysis of crop yield data with the form of spatial autocorrelation analysis can be used as a practical sustainable approach to locate statistically significant low-production areas. The resulted insights can be used as prescription maps on the tractors to reduce overall inputs and farming costs. This aim of this work is to present the benefits of conducting spatial analysis of yield crop data as a sustainable approach. Current work proves that the implementation of this process is costless, easy to perform and provides a better understanding of the current agronomic needs for better decision-making within a short time, adopting a sustainable approach.

scholarly journals The Relationship between Soil Electrical Parameters and Compaction of Sandy Clay Loam Soil

Agriculture ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 114
Author(s):  
Katarzyna Pentoś ◽  
Krzysztof Pieczarka ◽  
Kamil Serwata
Keyword(s):  
Electrical Conductivity ◽  
Magnetic Susceptibility ◽  
Precision Agriculture ◽  
Soil Compaction ◽  
Soil Layer ◽  
Soil Parameters ◽  
Soil Electrical Conductivity ◽  
Electrical Parameters ◽  
Management Zones ◽  
Additional Information

Soil spatial variability mapping allows the delimitation of the number of soil samples investigated to describe agricultural areas; it is crucial in precision agriculture. Electrical soil parameters are promising factors for the delimitation of management zones. One of the soil parameters that affects yield is soil compaction. The objective of this work was to indicate electrical parameters useful for the delimitation of management zones connected with soil compaction. For this purpose, the measurement of apparent soil electrical conductivity and magnetic susceptibility was conducted at two depths: 0.5 and 1 m. Soil compaction was measured for a soil layer at 0–0.5 m. Relationships between electrical soil parameters and soil compaction were modelled with the use of two types of neural networks—multilayer perceptron (MLP) and radial basis function (RBF). Better prediction quality was observed for RBF models. It can be stated that in the mathematical model, the apparent soil electrical conductivity affects soil compaction significantly more than magnetic susceptibility. However, magnetic susceptibility gives additional information about soil properties, and therefore, both electrical parameters should be used simultaneously for the delimitation of management zones.

scholarly journals Geostatistical analysis of crop yield maps in a long term no tillage system

Bragantia ◽  
2010 ◽  
Vol 69 (suppl) ◽  
pp. 9-18 ◽  
Author(s):  
Osvaldo Guedes Filho ◽  
Sidney Rosa Vieira ◽  
Marcio Koiti Chiba ◽  
Célia Regina Grego
Keyword(s):  
Crop Yield ◽  
Crop Yields ◽  
Spherical Model ◽  
Exponential Model ◽  
Spatial And Temporal Variability ◽  
No Tillage ◽  
Yield Data ◽  
The North ◽  
Tillage System

It is known, for a long time, that crop yields are not uniform at the field. In some places, it is possible to distinguish sites with both low and high yields even within the same area. This work aimed to evaluate the spatial and temporal variability of some crop yields and to identify potential zones for site specific management in an area under no-tillage system for 23 years. Data were analyzed from a 3.42 ha long term experimental area at the Centro Experimental Central of the Instituto Agronômico, located in Campinas, Sao Paulo State, Brazil. The crop yield data evaluated included the following crops: soybean, maize, lablab and triticale, and all of them were cultivated since 1985 and sampled at a regular grid of 302 points. Data were normalized and analyzed using descriptive statistics and geostatistical tools in order to demonstrate and describe the structure of the spatial variability. All crop yields showed high variability. All of them also showed spatial dependence and were fitted to the spherical model, except for the yield of the maize in 1999 productivity which was fitted to the exponential model. The north part of the area presented repeated high values of productivity in some years. There was a positive cross correlation amongst the productivity values, especially for the maize crops.

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