Site-specific management zones delineation and Yield prediction for rice based cropping system using on-farm data sets in Tolima (Colombia)

2020 ◽  
Author(s):  
Sofiane Ouazaa ◽  
Oscar Barrero ◽  
Yeison Mauricio Quevedo Amaya ◽  
Nesrine Chaali ◽  
Omar Montenegro Ramos
Keyword(s):  
Machine Learning ◽  
Chlorophyll Content ◽  
Rice Yield ◽  
Cropping System ◽  
Fuzzy Cluster ◽  
Sampling Point ◽  
Yield Prediction ◽  
Management Zones ◽  
On Farm ◽  
Relative Chlorophyll Content

<p>In the valley of the Alto Magdalena, Colombia, intensive agriculture and inefficient soil and water management techniques have generated a within field yield spatial variability, which have increased the production costs for the rice-based cropping system (rice, cotton and maize crops rotation field). Crop yield variations depend on the interaction between climate, soil, topography and management, and it is strongly influenced by the spatial and temporal availabilities of water and nutrients in the soil during the crop growth season. Understanding why the yield in certain portions of a field has a high variability is of paramount importance both from an economic and an environmental point of view, as it is through the better management of these areas that we can improve yields or reduce input costs and environmental impact. The aim of this study was 1) to predict rice yield using on farm data set and machine learning and 2) to compare delimited management zones (MZ) for rice-based cropping system with physiological parameters and within field variation yield.</p><p>A 72 sampling points spatially distributed were defined in a 5 hectares plot at the research center Nataima, Agrosavia. For each sampling point, physical and chemical properties, biomass and relative chlorophyll content were determined at different vegetative stages. A multispectral camera mounted to an Unmanned Aerial Vehicle (UAV) was used to acquire multispectral images over the rice canopy in order to estimate vegetation indices. Five nonlinear models and two multilinear algorithms were employed to estimate rice yield. The fuzzy cluster analysis algorithm was used to classify soil data into two to six MZ. The appropriate number of MZ was determined according to the results of a fuzziness performance index and normalized classification entropy.</p><p>Results of the rice yield prediction model showed that the best performance was obtained by K-Nearest Neighbors (KNN) regression algorithm with an average absolute error of 10.74%. Nonetheless, the performance of the other algorithms was acceptable except the Multiple Linear regression (MLR). The MLR showed the highest RMSE with 2712.26 kg.ha<sup>-1</sup> in the testing dataset, while KNN regression was the best with 1029.69 kg.ha<sup>-1</sup>. These findings show the importance of machine learning could have for supporting decisions in agriculture processes management.</p><p>The cluster analyses revealed that two zones was the optimal number of classes based on different criteria. Delineated zones were evaluated and revealed significant differences (p≤0.05) in sand, apparent density, total porosity, pH, organic matter, phosphorus, calcium, magnesium, iron, zinc, cover and boron. The relative chlorophyll content of cotton and maize crops showed a similar spatial distribution pattern to delimited MZ. The results demonstrate the ability of the proposed procedure to delineate a farmer’s field into zones based on spatially varying soil and crop properties that should be considered for irrigation and fertilization management.</p>

Rice Yield Prediction Using On-Farm Data Sets and Machine Learning

2020 ◽  
pp. 422-430
Author(s):  
Oscar Barrero ◽  
Sofiane Ouazaa ◽  
Camilo Ignacio Jaramillo-Barrios ◽  
Mauricio Quevedo ◽  
Nesrine Chaali ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rice Yield ◽  
Data Sets ◽  
Yield Prediction ◽  
On Farm

scholarly journals Machine learning model for rice yield prediction using KNN regression.

2021 ◽  
Author(s):  
Akhil Wilson ◽  
Raji Sukumar ◽  
N. Hemalatha
Keyword(s):  
Machine Learning ◽  
Rice Yield ◽  
Support Vector ◽  
Yield Prediction ◽  
Challenging Problem ◽  
Random Forest Regression ◽  
Machine Learning Model ◽  
Smart Farming ◽  
Regression Algorithms ◽  
The One

Abstract The prediction of agriculture yield is the one of the challenging problem in smart farming, we have predicted the yield of rice in the state of Kerala, India with the help of Machine Learning by considering the soil properties, micro climatic condition and area of the rice. Here we have used Decision Tree Regression, Random Forest Regression, Linear Regression, K Nearest Neighbour Regression, Xgboost Regression and Support Vector Regression algorithms in order to predict the rice yield. From the experiments we got KNN regression to be the best with 98.77% accuracy.

scholarly journals MACHINE LEARNING APPROACH FOR KHARIF RICE YIELD PREDICTION INTEGRATING MULTI-TEMPORAL VEGETATION INDICES AND WEATHER AND NON-WEATHER VARIABLES

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 187-194
Author(s):  
A. Chandra ◽  
P. Mitra ◽  
S. K. Dubey ◽  
S. S. Ray
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Rice Yield ◽  
Vegetation Indices ◽  
Growth Stages ◽  
Yield Prediction ◽  
Learning Approaches ◽  
Weather Variables ◽  
Data Set ◽  
Bankura District

<p><strong>Abstract.</strong> The development of kharif rice yield prediction models was attempted through Machine Learning approaches such as Artificial Neural Network and Random Forest for the 42 blocks covering 13,141&amp;thinsp;sq&amp;thinsp;km upland rainfed area of Purulia and Bankura district, West Bengal. Models were developed integrating monthly NDVI with weather and non-weather variables at block-level for the period 2006 to 2015. The model correlation obtained was 0.702 with MSE 0.01. Though the weather variables vs NDVI models are quite satisfactory, NDVI vs kharif rice yield models however, show relatively less correlation, about 0.6 revealing the requirement of varied additional farmer-controlled inputs. Development of NDVI vs crop yield models for different crop growth stages or fortnightly over a larger data set with selective adding of weather and non-weather variables to NDVI would be the most appropriate.</p>

Integrating Multi-Source Data for Rice Yield Prediction across China using Machine Learning and Deep Learning Approaches

2021 ◽  
Vol 297 ◽  
pp. 108275
Author(s):  
Juan Cao ◽  
Zhao Zhang ◽  
Fulu Tao ◽  
Liangliang Zhang ◽  
Yuchuan Luo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Rice Yield ◽  
Yield Prediction ◽  
Learning Approaches ◽  
Source Data

scholarly journals Corn Nitrogen Status Diagnosis with an Innovative Multi-Parameter Crop Circle Phenom Sensing System

2021 ◽  
Vol 13 (3) ◽  
pp. 401
Author(s):  
Cadan Cummings ◽  
Yuxin Miao ◽  
Gabriel Dias Paiao ◽  
Shujiang Kang ◽  
Fabián G. Fernández
Keyword(s):  
Machine Learning ◽  
Chlorophyll Content ◽  
Vegetation Index ◽  
Soil Drainage ◽  
Management Information ◽  
Area Index ◽  
Sensing System ◽  
Extreme Gradient Boosting ◽  
Split Plot ◽  
N Status

Accurate and non-destructive in-season crop nitrogen (N) status diagnosis is important for the success of precision N management (PNM). Several active canopy sensors (ACS) with two or three spectral wavebands have been used for this purpose. The Crop Circle Phenom sensor is a new integrated multi-parameter proximal ACS system for in-field plant phenomics with the capability to measure reflectance, structural, and climatic attributes. The objective of this study was to evaluate this multi-parameter Crop Circle Phenom sensing system for in-season diagnosis of corn (Zea mays L.) N status across different soil drainage and tillage systems under variable N supply conditions. The four plant metrics used to approximate in-season N status consist of aboveground biomass (AGB), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). A field experiment was conducted in Wells, Minnesota during the 2018 and the 2019 growing seasons with a split-split plot design replicated four times with soil drainage (drained and undrained) as main block, tillage (conventional, no-till, and strip-till) as split plot, and pre-plant N (PPN) rate (0 to 225 in 45 kg ha−1 increment) as the split-split plot. Crop Circle Phenom measurements alongside destructive whole plant samples were collected at V8 +/−1 growth stage. Proximal sensor metrics were used to construct regression models to estimate N status indicators using simple regression (SR) and eXtreme Gradient Boosting (XGB) models. The sensor derived indices tested included normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), estimated canopy chlorophyll content (eCCC), estimated leaf area index (eLAI), ratio vegetation index (RVI), canopy chlorophyll content index (CCCI), fractional photosynthetically active radiation (fPAR), and canopy and air temperature difference (ΔTemp). Management practices such as drainage, tillage, and PPN rate were also included to determine the potential improvement in corn N status diagnosis. Three of the four replicated drained and undrained blocks were randomly selected as training data, and the remaining drained and undrained blocks were used as testing data. The results indicated that SR modeling using NDVI would be sufficient for estimating AGB compared to more complex machine learning methods. Conversely, PNC, PNU, and NNI all benefitted from XGB modeling based on multiple inputs. Among different approaches of XGB modeling, combining management information and Crop Circle Phenom measurements together increased model performance for predicting each of the four plant N metrics compared with solely using sensing data. The PPN rate was the most important management metric for all models compared to drainage and tillage information. Combining Crop Circle Phenom sensor parameters and management information is a promising strategy for in-season diagnosis of corn N status. More studies are needed to further evaluate this new integrated sensing system under diverse on-farm conditions and to test other machine learning models.

A novel spectral index for estimation of relative chlorophyll content of sugar beet

2021 ◽  
Vol 184 ◽  
pp. 106088
Author(s):  
Jing Zhang ◽  
Haiqing Tian ◽  
Di Wang ◽  
Haijun Li ◽  
Abdul Mounem Mouazen
Keyword(s):  
Sugar Beet ◽  
Chlorophyll Content ◽  
Spectral Index ◽  
Relative Chlorophyll Content

Analysis of agricultural crop yield prediction using statistical techniques of machine learning

2021 ◽  
Author(s):  
Janmejay Pant ◽  
R.P. Pant ◽  
Manoj Kumar Singh ◽  
Devesh Pratap Singh ◽  
Himanshu Pant
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Statistical Techniques ◽  
Yield Prediction ◽  
Agricultural Crop

scholarly journals The Preferences of Different Cultivars of Lettuce Seedlings (Lactuca sativa L.) for the Spectral Composition of Light

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1211
Author(s):  
Barbara Frąszczak ◽  
Monika Kula-Maximenko
Keyword(s):  
Chlorophyll Content ◽  
Blue Light ◽  
White Light ◽  
Leaf Shape ◽  
Red Light ◽  
Spectral Composition ◽  
Fresh Weight ◽  
Light Spectrum ◽  
Dark Green ◽  
Relative Chlorophyll Content

The spectrum of light significantly influences the growth of plants cultivated in closed systems. Five lettuce cultivars with different leaf colours were grown under white light (W, 170 μmol m−2 s−1) and under white light with the addition of red (W + R) or blue light (W + B) (230 μmol m−2 s−1). The plants were grown until they reached the seedling phase (30 days). Each cultivar reacted differently to the light spectrum applied. The red-leaved cultivar exhibited the strongest plasticity in response to the spectrum. The blue light stimulated the growth of the leaf surface in all the plants. The red light negatively influenced the length of leaves in the cultivars, but it positively affected their number in red and dark-green lettuce. It also increased the relative chlorophyll content and fresh weight gain in the cultivars containing anthocyanins. When the cultivars were grown under white light, they had longer leaves and higher value of the leaf shape index. The light-green cultivars had a greater fresh weight. Both the addition of blue and red light significantly increased the relative chlorophyll content in the dark-green cultivar. The spectrum enhanced with blue light had positive influence on most of the parameters under analysis in butter lettuce cultivars. These cultivars were also characterised by the highest absorbance of blue light.

Performance of Cowpea and Horse Gram as Intercrops with Bt Cotton on Red Soils of Southern Transition Zone of Karnataka Plateau, India

2021 ◽  
Author(s):  
V. Ramamurthy ◽  
G. Sangeetha ◽  
B. Shyla
Keyword(s):  
Transition Zone ◽  
Production Efficiency ◽  
Management Practice ◽  
Cropping System ◽  
Bt Cotton ◽  
Cotton Yield ◽  
Large Area ◽  
Horse Gram ◽  
Red Soils ◽  
On Farm

Background: Horizontal expansion of area under pulses at country level has very little possibilities. This necessitates exploring alternate ways to increase the area and production of pulses. Bt cotton is the major cash crop grown in large area in Southern transition zone of Karnataka on red soils. Bt cotton hybrids are sown at wide row spacing hence provide sufficient space for cultivation of short duration pulses like cowpea and horse gram.Methods: On-farm trials were carried out in medium deep red soils of Basavanagiri village of Mysore district, Karnataka during 2014-15 and 2015-16. There were six treatments consists of Bt cotton with farmers practice (T1), Bt cotton with best management practice (T2), sole cowpea (T3), sole horse gram (T4), Bt cotton intercropped with cowpea (T5) and Bt cotton inter cropped with horse gram (T6). On-farm trials were laid out by using RCBD design in five farmer fields, which served as replications.Result: On-farm investigation indicated that there was no much difference between cotton yield sole crop with BMP and inter cropped cotton yield. However, cotton yield was significantly lower in farmers practice over BMP. Intercropping of cowpea and horse gram with Bt cotton resulted in higher cotton equivalent yield, LER and production efficiency over the sole cotton cropping system. This was due to the wider spacing of the cotton and better resource use efficiency in intercropping system.

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