scholarly journals Crop Recommendation System using Machine Learning

2021 ◽  
pp. 558-569
Author(s):  
Dhruvi Gosai ◽  
Chintal Raval ◽  
Rikin Nayak ◽  
Hardik Jayswal ◽  
Axat Patel
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Soil Degradation ◽  
Crop Production ◽  
Recommendation System ◽  
Machine Learning Algorithms ◽  
Soil Testing ◽  
Soil Parameters ◽  
Monitoring Temperature ◽  
Crop Health

A vast fraction of the population of India considers agriculture as its primary occupation. The production of crops plays an important role in our country. Bad quality crop production is often due to either excessive use of fertilizer or using not enough fertilizer. The proposed system of IoT and ML is enabled for soil testing using the sensors, is based on measuring and observing soil parameters. This system lowers the probability of soil degradation and helps maintain crop health. Different sensors such as soil temperature, soil moisture, pH, NPK, are used in this system for monitoring temperature, humidity, soil moisture, and soil pH along with NPK nutrients of the soil respectively. The data sensed by these sensors is stored on the microcontroller and analyzed using machine learning algorithms like random forest based on which suggestions for the growth of the suitable crop are made. This project also has a methodology that focuses on using a convolutional neural network as a primary way of identifying if the plant is at risk of a disease or not.

scholarly journals Crop Recommendation on Analyzing Soil Using Machine Learning

2021 ◽  
Vol 12 (6) ◽  
pp. 1784-1791
Author(s):  
Anguraj.K Et al.
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Data Analysis ◽  
Graphical User Interface ◽  
Crop Production ◽  
New Technologies ◽  
Machine Learning Algorithms ◽  
Soil Parameters ◽  
Traditional Farming ◽  
Precise Time

Agriculture plays a significant role in increasing the economic development of our nation. Crop production has greatly affected due to changes in weather pattern. Emerging technologies can be used to improve productivity of the crops by converting traditional farming to precision farming. The new technologies that are used include data analysis and Internet of things (IOT). The major issue yet to be resolved is cultivating precise crop at precise time. This can be done with the help machine learning algorithms which is found to be an effective method for predicting the suitable crop. The soil parameters such as soil moisture, temperature, humidity and pH are collected from the sensors using IOT and given to Graphical User Interface (GUI). GUI gets the inputs and suggests the suitable crops. The system developed using IOT and ML greatly helps the farmers to take a valuable decision.

Spatiotemporal modeling of soil moisture in an Atlantic forest through machine learning algorithms

2021 ◽  
Author(s):  
Vinicius Augusto Oliveira ◽  
André Ferreira Rodrigues ◽  
Marco Antônio Vieira Morais ◽  
Marcela de Castro Nunes Santos Terra ◽  
Li Guo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Atlantic Forest ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Spatiotemporal Modeling

scholarly journals Movie Recommenadation System

2021 ◽  
Vol 9 (4) ◽  
pp. 13-16
Author(s):  
Gandhali Malve ◽  
Lajree Lohar ◽  
Tanay Malviya ◽  
Shirish Sabnis
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Recommendation Systems ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Amount Of Information ◽  
System Recommendation ◽  
User Ratings

Today the amount of information in the internet growth very rapidly and people need some instruments to find and access appropriate information. One of such tools is called recommendation system. Recommendation systems help to navigate quickly and receive necessary information. Many of us find it difficult to decide which movie to watch and so we decided to make a recommender system for us to better judge which movie we are more likely to love. In this project we are going to use Machine Learning Algorithms to recommend movies to users based on genres and user ratings. Recommendation system attempt to predict the preference or rating that a user would give to an item.

scholarly journals Earth Observation Data for Agricultural Drought Monitoring in the Pannonian Basin

2020 ◽  
Author(s):  
Laura Crocetti ◽  
Milan Fischer ◽  
Matthias Forkel ◽  
Aleš Grlj ◽  
Wai-Tim Ng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Pannonian Basin ◽  
Spatial Scales ◽  
European Space Agency ◽  
Earth Observation ◽  
Machine Learning Algorithms ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Observation Data

<p>The Pannonian Basin is a region in the southeastern part of Central Europe that is heavily used for agricultural purposes. It is geomorphological defined as the plain area that is surrounded by the Alps in the west, the Dinaric Alps in the Southwest, and the Carpathian mountains in the North, East and Southeast. In recent decades, the Pannonian Basin has experienced several drought episodes, leading to severe impacts on the environment, society, and economy. Ongoing human-induced climate change, characterised by increasing temperature and potential evapotranspiration as well as changes in precipitation distribution will further exacerbate the frequency and intensity of extreme events. Therefore, it is important to monitor, model, and forecast droughts and their impact on the environment for a better adaption to the changing weather and climate extremes. The increasing availability of long-term Earth observation (EO) data with high-resolution, combined with the progress in machine learning algorithms and artificial intelligence, are expected to improve the drought monitoring and impact prediction capacities.</p><p>Here, we assess novel EO-based products with respect to drought processes in the Pannonian Basin. To identify meteorological and agricultural drought, the Standardized Precipitation-Evapotranspiration Index was computed from the ERA5 meteorological reanalysis and compared with drought indicators based on EO time series of soil moisture and vegetation like the Soil Water Index or the Normalized Difference Vegetation Index. We suggest that at resolution representing the ERA5 reanalysis (~0.25°) or coarser, both meteorological as well as EO data can identify drought events similarly well. However, at finer spatial scales (e.g. 1 km) the variability of biophysical properties between fields cannot be represented by meteorological data but can be captured by EO data. Furthermore, we analyse historical drought events and how they occur in different EO datasets. It is planned to enhance the forecasting of agricultural drought and estimating drought impacts on agriculture through exploiting the potential of EO soil moisture and vegetation data in a data-driven machine learning framework.</p><p>This study is funded by the DryPan project of the European Space Agency (https://www.eodc.eu/esa-drypan/).</p>

Relative cost to soil fertility of long-term crop production without fertilization

1996 ◽  
Vol 76 (3) ◽  
pp. 401-406 ◽  
Author(s):  
C. A. Campbell ◽  
F. Selles ◽  
J. T. Harapiak ◽  
G. P. Lafond
Keyword(s):  
Soil Moisture ◽  
Soil Fertility ◽  
Green Manure ◽  
Soil Degradation ◽  
Crop Production ◽  
Cropping Systems ◽  
Triticum Aestivum L ◽  
Growth Chamber ◽  
Rotation Phase ◽  
N Fertility

An earlier analysis of yield trends of stubble-wheat in six cropping systems, over 35 yr, in a thin Black Chernozemic soil at Indian Head, Saskatchewan, showed that fertilizer improved soil quality, while absence of fertilizer, combined with frequent fallowing, led to soil degradation. The inclusion of a legume green manure crop in the rotation failed to maintain soil fertility, apparently because legumes do not supply P. Because the fertility and stored moisture effects were confounded, we conducted a growth chamber experiment to quantify soil responses to N and P in these six cropping systems. Soil from the top 15-cm of the rotation phase that had just grown two successive wheat (Triticum aestivum L.) crops was used. Various factorial combinations of ammonium nitrate-N and triple superphosphate-P were applied at N/P2O5 rates up to 200/200 kg ha−1. Soil moisture was maintained in the available range. Regression analysis showed that the fallow-wheat-wheat (F-W-W) and continuous wheat (Cont W) systems that had not been fertilized in 35 yr, and which had moderate amounts of NaHCO3-P, only responded to N. In contrast, the green manure (GM)- and hay (H)- containing systems, which had also not been fertilized before had low levels of NaHCO3-P and responded to both N and P. In the field, the yields of wheat grown on stubble in 1991 rated: Cont W (N + P) > F-W-W (N + P) > F-W-W-H-H-H > Cont W > GM-W-W > F-W-W. However, in the growth chamber the rating was: Cont W (N + P) > F-W-W-H-H-H > GM-W-W > Cont W > F-W-W (N + P) > F-W-W. We suggest that the growth chamber results more accurately reflect the present fertility status of these soils, because fertility is no longer confounded with soil moisture. Grain yields in the growth chamber were directly proportional to the previously measured initial potential rate of N mineralization, indicating the value of the latter parameter as a useful index of soil N fertility. Key words: Nitrogen, phosphorus, soil degradation, legumes, fertilizers

scholarly journals Impact of Weather on Crops in Few Northern Parts of Bangladesh: HCI and Machine Learning Based Approach

2020 ◽  
pp. 7-15
Keyword(s):  
Machine Learning ◽  
Food Security ◽  
Crop Production ◽  
Performance Metrics ◽  
Machine Learning Algorithms ◽  
Production Level ◽  
Soft System Methodology ◽  
Average Production ◽  
The Impact ◽  
System Methodology

As Bangladesh is an agricultural country, the economy, as well as the food security of this country, mostly depends on the production level of different crops over the year. Therefore, there exists immense pressure on exaggerated crop production due to the fast growth of the population. But, the average production level is being hampered by the bad nature of the weather. We have conducted a survey on near about 100 farmers of two northern districts of Bangladesh: Pabna and Rajshahi and assessed the impact of rough nature on production. According to farmers and agriculturalists, it is noticed that rough weather causes about 30% to 70% production shortage than expectation with all other factors remaining constant. In this study, we have adopted Human-computer interaction (HCI) based approach (Soft System Methodology-SSM) to this aspect for efficacious collaboration with root-level farmers and agricultural trainers providing ease for understanding weather-related issues on the production of crops. Finally, some machine learning algorithms were also implemented on the obtained dataset to accurately classify the range of production level of rice and a comparison is made among the algorithms based on performance metrics. Moreover, an android based application is created to depict the summary of the study.

scholarly journals Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6926 ◽  
Author(s):  
Xiangyu Ge ◽  
Jingzhe Wang ◽  
Jianli Ding ◽  
Xiaoyi Cao ◽  
Zipeng Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Moisture Content ◽  
Arid Regions ◽  
Soil Moisture Content ◽  
Hyperspectral Imagery ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Spectral Indices ◽  
Processing Scheme

Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model’s SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0–10 cm) from the farmland (2.5 × 104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R2val = 0.907, RMSEP = 1.477, and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R2val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions.

scholarly journals Ground Observations and Environmental Covariates Integration for Mapping of Soil Salinity: A Machine Learning-Based Approach

2021 ◽  
Vol 13 (23) ◽  
pp. 4825
Author(s):  
Salman Naimi ◽  
Shamsollah Ayoubi ◽  
Mojtaba Zeraatpisheh ◽  
Jose Alexandre Melo Dematte
Keyword(s):  
Machine Learning ◽  
Soil Salinity ◽  
Crop Production ◽  
Soil Salinization ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Agricultural Activity ◽  
Ensemble Modeling ◽  
Least Squares Regression ◽  
Environmental Covariates

Soil salinization is a severe danger to agricultural activity in arid and semi-arid areas, reducing crop production and contributing to land destruction. This investigation aimed to utilize machine learning algorithms to predict spatial soil salinity (dS m−1) by combining environmental covariates derived from remotely sensed (RS) data, a digital elevation model (DEM), and proximal sensing (PS). The study is located in an arid region, southern Iran (52°51′–53°02′E; 28°16′–28°29′N), in which we collected 300 surface soil samples and acquired the spectral data with RS (Sentinel-2) and PS (electromagnetic induction instrument (EMI) and portable X-ray fluorescence (pXRF)). Afterward, we analyzed the data using five machine learning methods as follows: random forest—RF, k-nearest neighbors—kNN, support vector machines—SVM, partial least squares regression—PLSR, artificial neural networks—ANN, and the ensemble of individual models. To estimate the electrical conductivity of the saturated paste extract (ECe), we built three scenarios, including Scenario (1): Synthetic Soil Image (SySI) bands and salinity indices derived from it; Scenario (2): RS data, PS data, topographic attributes, and geology and geomorphology maps; and Scenario (3): the combination of Scenarios (1) and (2). The best prediction accuracy was obtained for the RF model in Scenario (3) (R2 = 0.48 and RMSE = 2.49), followed by Scenario (2) (RF model, R2 = 0.47 and RMSE = 2.50) and Scenario (1) for the SVM model (R2 = 0.26 and RMSE = 2.97). According to ensemble modeling, a combined strategy with the five models exceeded the performance of all the single ones and predicted soil salinity in all scenarios. The results revealed that the ensemble modeling method had higher reliability and more accurate predictive soil salinity than the individual approach. Relative improvement (RI%) showed that the R2 index in the ensemble model improved compared to the most precise prediction for the Scenarios (1), (2), and (3) with 120.95%, 56.82%, and 66.71%, respectively. We applied the best model in each scenario for mapping the soil salinity in the selected area, which indicated that ECe tended to increase from the northwestern to south and southeastern regions. The area with high ECe was located in the regions that mainly had low elevations and playa. The areas with low ECe were located in the higher elevations with steeper slopes and alluvial fans, and thus, relief had great importance. This study provides a precise, cost-effective, and scientific base prediction for decision-making purposes to map soil salinity in arid regions.

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