scholarly journals Quantifying the effect of environments on crop emergence, development and yield using sensing and deep learning techniques

2021 ◽  
Author(s):  
◽  
Aijing Feng
Keyword(s):  
Deep Learning ◽  
Soil Texture ◽  
Precision Agriculture ◽  
Clay Content ◽  
Weather Conditions ◽  
Growth Stages ◽  
Cotton Yield ◽  
Field Scale ◽  
Learning Techniques ◽  
Crop Development

The world population is estimated to increase by 2 billion in the next 30 years, and global crop production needs to double by 2050 to meet the projected demands from rising population, diet shifts, and increasing biofuels consumption. Improving the production of the major crops has become an increasing concern for the global research community. However, crop development and yield are complex and determined by many factors, such as crop genotypes (varieties), growing environments (e.g., weather, soil, microclimate and location), and agronomic management strategies (e.g., seed treatment and placement, planting, fertilizer and pest management). To develop next-generation and high-efficiency agriculture production systems, we will have to solve the complex equation consisting of the interactions of genotype, environment and management (GxExM) using emerging technologies. Precision agriculture is a promising agriculture practice to increase profitability and reduce environmental impact using site-specific and accurate measurement of crop, soil and environment. The success of precision agriculture technology heavily relies on access to accurate and high-resolution spatiotemporal data and reliable prediction models of crop development and yield. Soil texture and weather conditions are important factors related to crop growth and yield. The percentages of sand, clay and silt in the soil affect the movement of air and water, as well as the water holding capacity. Weather conditions, including temperature, wind, humidity and solar irradiance, are determining factors for crop evapotranspiration and water requirements. Compared to crop yield, which is easy to measure and quantify, crop development effects due to the soil texture and weather conditions within a season can be challenging to measure and quantify. Evaluation of crop development by visual observation at field scale is time-consuming and subjective. In recent years, sensor-based methods have provided a promising way to measure and quantify crop development. Unmanned aerial vehicles (UAVs) equipped with visual sensors, multispectral sensors and/or hyperspectral sensors have been used as a high-throughput data collection tool by many researchers to monitor crop development efficiently at the desired time and at field-scale. In this study, UAV-based remote sensing technologies combining with soil texture and weather conditions were used to study the crop emergence, crop development and yield under the effects of varying soil texture and weather conditions in a cotton research field. Soil texture, i.e., sand and clay content, calculated using apparent soil electrical conductivity (EC [subscript a]) based on a model from a previous study, was used to estimate soil characteristics, including field capacity, wilting point and total available water. Weather data were obtained from a weather station 400 m from the field. UAV imagery data were collected using a high-resolution RGB camera, a multispectral camera and a thermal camera from the crop emergence to before harvesting on a monthly basis. An automatic method to count emerged crop seedlings based on image technologies and a deep learning model was developed for near real-time cotton emergence evaluation. The soil and elevation effects on the stand count and seedling size were explored. The effects of soil texture and weather conditions on cotton growth variation were examined using multispectral images and thermal images during the crop development growth stages. The cotton yield variations due to soil texture and weather conditions were estimated using multiple-year UAV imagery data, soil texture, weather conditions and deep learning techniques. The results showed that field elevation had a high impact on cotton emergence (stand count and seedling size) and clay content had a negative impact on cotton emergence in this study. Monthly growth variations of cotton under different soil textures during crop development growth stages were significant in both 2018 and 2019. Soil clay content in shallow layers (0-40 cm) affected crop development in early growth stages (June and July) while clay content in deep layers (40-70 cm) affected the mid-season growth stages (August and September). Thermal images were more efficient in identifying regions of water stress compared to the water stress coefficient Ks calculated using data of soil texture and weather conditions. Results showed that cotton yield for each one of the three years (2017-2019) could be predicted using the model trained with data of the other two years with prediction errors of MAE = 247 (8.9 [percent]) to 384 kg ha [superscript -1] (13.7 [percent]), which showed that quantifying yield variability for a future year based on soil texture, weather conditions and UAV imagery was feasible. Results from this research indicated that the integration of soil and weather information and UAV-based image data is a promising way to understand the effects of soil and weather on crop emergence, crop development and yield.

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.

Soil properties: their prediction and feature extraction from the LUCAS spectral library using deep convolutional neural networks

2021 ◽  
Author(s):  
Liang Zhong ◽  
Xi Guo ◽  
Zhe Xu ◽  
Meng Ding
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Soil Properties ◽  
Convolutional Neural Networks ◽  
Soil Texture ◽  
Precision Agriculture ◽  
Organic Carbon Content ◽  
Spectral Library ◽  
Deep Convolutional Neural Networks ◽  
Large Soil

<p>Soil, as a non-renewable resource, should be monitored continuously to prevent its degradation and promote sustainable agricultural management. Soil spectroscopy in the visible-near infrared range is a fast and cost-effective analytical technique to predict soil properties. The use of large soil spectral libraries can reduce the work needed to reliably estimate soil properties and obtain robust models capable of widespread applicability. Deep learning is apt for big data analysis, and this approach could herald a profound change in the way we model soil spectral data generally. Accordingly, we explored the modeling potential of deep convolutional neural networks (DCNNs) for soil properties based on a large soil spectral library. The European topsoil dataset provided by the Land Use/Cover Area frame Survey (LUCAS) was used without any pre-processing of spectra or covariates added. Two 16-layer DCNN models (ResNet-16 and VGGNet-16) were successfully used to make regression predictions of seven soil properties and classification predictions of soil texture into four groups and 12 levels. Our results showed that the ResNet-16 and VGGNet-16 models produced highly accurate predictions for most soil properties, being superior to either a shallow convolutional neural network and traditional machine learning approaches. Soil organic carbon content, nitrogen content, cation exchange capacity, pH, and calcium carbonate content were well predicted, having a ratio of performance to deviation (RPD) > 2.0. Soil potassium content was adequately predicted (1.4 ≤ RPD ≤ 2.0) and phosphorous content was poorly predicted (RPD < 1.4). The overall classification accuracy of soil texture was 0.749 (four groups) and 0.566 (12 levels). The position of feature wavelengths differed among the soil properties, for which multiple characteristic peaks were common. This study fully demonstrates the modeling potential of deep learning with soil hyperspectral data, which could bring us closer to achieving precision agriculture.</p>

WEATHER AT DIFFERENT GROWTH STAGES, MULTIPLE PRACTICES AND RISK EXPOSURES: PANEL DATA EVIDENCE FROM ETHIOPIA

2020 ◽  
Vol 11 (02) ◽  
pp. 2050009
Author(s):  
HAILEMARIAM TEKLEWOLD ◽  
ALEMU MEKONNEN
Keyword(s):  
Agricultural Practices ◽  
Weather Conditions ◽  
Crop Growth ◽  
Growth Stages ◽  
Production Risk ◽  
Heterogeneous Effects ◽  
Crop Development ◽  
Crop Growth Stages ◽  
Variable Weather ◽  
Risk Reducing

This study investigates the effects of combinations of climate smart agricultural practices on risk exposure and cost of risk. We do this by examining the different risk components — mean, variance, skewness, and kurtosis — in a multinomial treatment effects framework by controlling weather variables for key stages of crop growth. We found that adoption of combinations of practices is widely viewed as a risk-reducing insurance strategy that can increase farmers’ resilience to production risk. The hypothesis of equality of weather parameters across crop development stages is also rejected. The heterogeneous effects of weather across crop growth stages have important implications for climate change adaptation to maximize quasi-option value. For a country that has the vision to build a climate-resilient economy, this knowledge is valuable to identify a combination of climate smart practices that minimizes production risk under variable weather conditions.

scholarly journals Deep Learning Application in Plant Stress Imaging: A Review

2020 ◽  
Vol 2 (3) ◽  
pp. 430-446 ◽  
Author(s):  
Zongmei Gao ◽  
Zhongwei Luo ◽  
Wen Zhang ◽  
Zhenzhen Lv ◽  
Yanlei Xu
Keyword(s):  
Deep Learning ◽  
Precision Agriculture ◽  
Plant Stress ◽  
Economic Loss ◽  
Machine Learning Techniques ◽  
Plant Phenotyping ◽  
Learning Approaches ◽  
Stress Imaging ◽  
Learning Techniques ◽  
Learning Principles

Plant stress is one of major issues that cause significant economic loss for growers. The labor-intensive conventional methods for identifying the stressed plants constrain their applications. To address this issue, rapid methods are in urgent needs. Developments of advanced sensing and machine learning techniques trigger revolutions for precision agriculture based on deep learning and big data. In this paper, we reviewed the latest deep learning approaches pertinent to the image analysis of crop stress diagnosis. We compiled the current sensor tools and deep learning principles involved in plant stress phenotyping. In addition, we reviewed a variety of deep learning applications/functions with plant stress imaging, including classification, object detection, and segmentation, of which are closely intertwined. Furthermore, we summarized and discussed the current challenges and future development avenues in plant phenotyping.

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

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