Yield and Quality Prediction of Winter Rapeseed — Artificial Neural Network and Random Forest Models

open7siThis research was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia, grant numbers 451-03-9/2021-14/200051, 451-03-9/2021-14/200134, 451-03-68/2020-14/ 200032 and 451-03-9/2021-14/200032.As one of the greatest agricultural challenges, yield prediction is an important issue for producers, stakeholders, and the global trade market. Most of the variation in yield is attributed to environmental factors such as climate conditions, soil type and cultivation practices. Artificial neural networks (ANNs) and random forest regression (RFR) are machine learning tools that are used unambiguously for crop yield prediction. There is limited research regarding the application of these mathematical models for the prediction of rapeseed yield and quality. A four-year study (2015–2018) was carried out in the Republic of Serbia with 40 winter rapeseed genotypes. The field trial was designed as a randomized complete block design in three replications. ANN, based on the Broyden–Fletcher–Goldfarb–Shanno iterative algorithm, and RFR models were used for prediction of seed yield, oil and protein yield, oil and protein content, and 1000 seed weight, based on the year of production and genotype. The best production year for rapeseed cultivation was 2016, when the highest seed and oil yield were achieved, 2994 kg/ha and 1402 kg/ha, respectively. The RFR model showed better prediction capabilities compared to the ANN model (the r2 values for prediction of output variables were 0.944, 0.935, 0.912, 0.886, 0.936 and 0.900, for oil and protein content, seed yield, 1000 seed weight, oil and protein yield, respectively).openRajkovic D.; Jeromela A.M.; Pezo L.; Loncar B.; Zanetti F.; Monti A.; Spika A.K.Rajkovic D.; Jeromela A.M.; Pezo L.; Loncar B.; Zanetti F.; Monti A.; Spika A.K

Science Takes Flight: Detection of Black Leg on Turnip Gray Mold on Hemp

Disease detection through traditional techniques such as scouting fields on foot, molecular assays, or morphological identification of plant pathogens is time consuming and costly. Disease diagnosis in the field can be extremely subjective, and largely depends on the experience and knowledge of pathogen identification and disease quantification. This thesis provides an evaluation of remote sensing for assessing disease in agricultural field settings via an unmanned aerial vehicle and machine learning. Case studies are presented on two different fungal diseases important in western Oregon crop production: black leg on turnip (incited by Leptosphaeria spp.) and gray mold on hemp (caused by Botrytis spp.). Both case studies utilized a support vector machine model to classify pixels of digital images collected with a multispectral Micasense RedEdge-M optical sensor. Turnip leaves were imaged at 1.5 m in situ while hemp plant images were collected by an unmanned aerial vehicle with flights at 10 m ex situ. Detection of pixels exemplifying black leg leaf spot symptoms on turnip leaves had an overall accuracy of 97.0% with a model sensitivity of 0.48. The support vector machine model utilized in gray mold detection on hemp incorporated a novel vegetation index, a modified green-red vegetation index along with the triangular greenness index, to identify pixels of diseased hemp inflorescences extracted from background soil and vegetation. The model had an overall accuracy of 95.8% when identifying a hemp inflorescence as diseased or non-diseased. False negatives were found to be high with a sensitivity of 0.70 in the hemp model. Additionally, gray mold disease incidence determined using the support vector machine model was compared with disease assessments collected by scouting on foot and was found to have similar treatment rankings, although the differences in the relative percentages between the two methods were found to be large. The findings of this study provide the foundation for further development of remote sensing techniques for black leg disease assessments in Brassica crops and potential deployment of remote sensing strategies for measuring gray mold in hemp fields

Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review

Disease diagnosis is one of the major tasks for increasing food production in agriculture. Although precision agriculture (PA) takes less time and provides a more precise application of agricultural activities, the detection of disease using an Unmanned Aerial System (UAS) is a challenging task. Several Unmanned Aerial Vehicles (UAVs) and sensors have been used for this purpose. The UAVs’ platforms and their peripherals have their own limitations in accurately diagnosing plant diseases. Several types of image processing software are available for vignetting and orthorectification. The training and validation of datasets are important characteristics of data analysis. Currently, different algorithms and architectures of machine learning models are used to classify and detect plant diseases. These models help in image segmentation and feature extractions to interpret results. Researchers also use the values of vegetative indices, such as Normalized Difference Vegetative Index (NDVI), Crop Water Stress Index (CWSI), etc., acquired from different multispectral and hyperspectral sensors to fit into the statistical models to deliver results. There are still various drifts in the automatic detection of plant diseases as imaging sensors are limited by their own spectral bandwidth, resolution, background noise of the image, etc. The future of crop health monitoring using UAVs should include a gimble consisting of multiple sensors, large datasets for training and validation, the development of site-specific irradiance systems, and so on. This review briefly highlights the advantages of automatic detection of plant diseases to the growers

Drone Technology in Precision Agriculture: Are There No Environmental Concerns?

The adoption of drones in precision agriculture is expanding at a rapid rate, and expected to rise even faster as improvements in the technology result in cheaper models. Studies on the economic impact of drone technology in precision agriculture present optimistic projections of increased global food production. But increased food production almost always comes with significant environmental concerns. This paper examines the environmental concerns of drone technology in precision agriculture. The methodology of this paper is theoretical analysis and extrapolation of current literature in order to reveal the gap which future research needs to fill. While proposing a new area that has not received the close attention of experts and researchers, the paper reveals future scenarios of environmental issues around the various methods of drone applications in agricultural practices. Keywords: Drone technology, precision agriculture, agricultural practices, environmental impact, food security words DOI: 10.7176/JEES/10-9-08 Publication date:September 30th 202

Yield and Quality Prediction of Winter Rapeseed—Artificial Neural Network and Random Forest Models

As one of the greatest agricultural challenges, yield prediction is an important issue for producers, stakeholders, and the global trade market. Most of the variation in yield is attributed to environmental factors such as climate conditions, soil type and cultivation practices. Artificial neural networks (ANNs) and random forest regression (RFR) are machine learning tools that are used unambiguously for crop yield prediction. There is limited research regarding the application of these mathematical models for the prediction of rapeseed yield and quality. A four-year study (2015–2018) was carried out in the Republic of Serbia with 40 winter rapeseed genotypes. The field trial was designed as a randomized complete block design in three replications. ANN, based on the Broyden–Fletcher–Goldfarb–Shanno iterative algorithm, and RFR models were used for prediction of seed yield, oil and protein yield, oil and protein content, and 1000 seed weight, based on the year of production and genotype. The best production year for rapeseed cultivation was 2016, when the highest seed and oil yield were achieved, 2994 kg/ha and 1402 kg/ha, respectively. The RFR model showed better prediction capabilities compared to the ANN model (the r2 values for prediction of output variables were 0.944, 0.935, 0.912, 0.886, 0.936 and 0.900, for oil and protein content, seed yield, 1000 seed weight, oil and protein yield, respectively)

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

Intra-field Nitrogen Estimation for Wheat and Corn using Unmanned Aerial Vehicle-based and Satellite Multispectral Imagery, Plant Biophysical Variables, Field Properties, and Machine Learning Methods

Management of nitrogen (N) fertilizers is an important agricultural practice and field of research to increase productivity, minimize environmental impacts and the cost of production. To apply N fertilizer at the right rate, time, and place depends on the crop type, desired yield, and field conditions. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral imagery, PlanetScope satellite imagery, vegetation indices (VI), crop height, leaf area index (LAI), field topographic metrics, and soil properties to predict canopy nitrogen weight (g/m2) of corn and wheat fields in southwestern Ontario, Canada. Random Forests (RF) and Support Vector Regression (SVR) machine learning models were tested with combinations of variable datasets and evaluated for accuracy of canopy nitrogen weight prediction. The results demonstrate that UAV and satellite-based prediction models including spectral variables, crop biophysical parameters, and field conditions can provide accurate and useful information for fertilizer management