Aerial Monitoring of Rice Crop Variables using an UAV Robotic System

This paper presents the integration of an UAV for the autonomous monitoring of rice crops. The system integrates image processing and machine learning algorithms to analyze multispectral aerial imagery. Our approach calculates 8 vegetation indices from the images at each stage of rice growth: vegetative, reproductive and ripening. Multivariable regressions and artificial neural networks have been implemented to model the relationship of these vegetation indices against two crop variables: biomass accumulation and leaf nitrogen concentration. Comprehensive experimental tests have been conducted to validate the setup. The results indicate that our system is capable of estimating biomass and nitrogen with an average correlation of 80% and 78% respectively

Wavelength Selection Method Based on Partial Least Square from Hyperspectral Unmanned Aerial Vehicle Orthomosaic of Irrigated Olive Orchards

Identifying and mapping irrigated areas is essential for a variety of applications such as agricultural planning and water resource management. Irrigated plots are mainly identified using supervised classification of multispectral images from satellite or manned aerial platforms. Recently, hyperspectral sensors on-board Unmanned Aerial Vehicles (UAV) have proven to be useful analytical tools in agriculture due to their high spectral resolution. However, few efforts have been made to identify which wavelengths could be applied to provide relevant information in specific scenarios. In this study, hyperspectral reflectance data from UAV were used to compare the performance of several wavelength selection methods based on Partial Least Square (PLS) regression with the purpose of discriminating two systems of irrigation commonly used in olive orchards. The tested PLS methods include filter methods (Loading Weights, Regression Coefficient and Variable Importance in Projection); Wrapper methods (Genetic Algorithm-PLS, Uninformative Variable Elimination-PLS, Backward Variable Elimination-PLS, Sub-window Permutation Analysis-PLS, Iterative Predictive Weighting-PLS, Regularized Elimination Procedure-PLS, Backward Interval-PLS, Forward Interval-PLS and Competitive Adaptive Reweighted Sampling-PLS); and an Embedded method (Sparse-PLS). In addition, two non-PLS based methods, Lasso and Boruta, were also used. Linear Discriminant Analysis and nonlinear K-Nearest Neighbors techniques were established for identification and assessment. The results indicate that wavelength selection methods, commonly used in other disciplines, provide utility in remote sensing for agronomical purposes, the identification of irrigation techniques being one such example. In addition to the aforementioned, these PLS and non-PLS based methods can play an important role in multivariate analysis, which can be used for subsequent model analysis. Of all the methods evaluated, Genetic Algorithm-PLS and Boruta eliminated nearly 90% of the original spectral wavelengths acquired from a hyperspectral sensor onboard a UAV while increasing the identification accuracy of the classification

Nitrogen and Biomass Estimation in Rice Crops through the Use of Machine Learning Models

El uso de imágenes Vehículos Aéreos no tripulados (UAV) para la estimación de biomasa y nitrógeno es un enfoque prometedor para la investigación en agricultura de precisión. Aprovechando la alta resolución espacial y espectral de las imágenes de UAV, los investigadores pueden obtener estimaciones precisas y detalladas de estas importantes variables, proporcionando información valiosa para optimizar las prácticas de gestión y comprender la dinámica de los cultivos de arroz. El modelo de biomasa y nitrógeno en el arroz es una área de investigación importante en seguridad alimentaria, ya que el arroz es un cultivo básico que alimenta a más de la mitad de la población mundial . Estimar con precisión la biomasa y el contenido de nitrógeno en el arroz puede ayudar a optimizar las prácticas de gestión de cultivos, aumentar los rendimientos y reducir el impacto medioambiental. El desarrollo de esta estimación mediante métodos no invasivos, como la estimación de parámetros mediante imágenes multiespectrales permite optimizar los tiempos de estimación y monitorizar el cultivo de forma automatizada. Se han aplicado diferentes técnicas de aprendizaje automático para relacionar los Indices Vegetativos (VIs) con la biomasa y el nitrógeno, entre estas técnicas se encuentran la regresión multivariante lineal y no lineal, máquinas de soporte vectorial SVM y redes neuronales NN, y otras no tan exploradas como árboles de decisión, conjuntos de regresión y los procesos de regresión gaussiana. Este trabajo explora la estimación de biomasa y nitrógeno en 59 parcelas de arroz mediante imágenes multiespectrales capturadas a 20 metros de altura. El experimento pretende 1) Caracterizar los parámetros de biomasa y nitrógeno en diferentes genotipos de cultivos de arroz en el Tolima a partir de las bases de datos del ecosistema Omicas; 2) Implementar diferentes algoritmos de estimación con los datos obtenidos de las imágenes de vehículos aéreos no tripulados de los cultivos del Tolima, y evaluarlos mediante métricas de regresión; 3) Diseñar un modelo de estimación para el comportamiento de la biomasa y el nitrógeno en los cultivos de estudio que integre los parámetros de las etapas fenológicas vegetativa, reproductiva y de maduración; y 4) Evaluar el desempeño del modelo de estimación de biomasa y nitrógeno mediante el cálculo de métricas de regresión obtenidas de la comparación entre los métodos tradicionales de medición y el procesamiento de imágenes.OMICASThe use of Unmanned Aerial Vehicles (UAV) images for biomass and nitrogen estimation is a promising approach for precision agriculture research. By leveraging the high spatial and spectral resolution of UAV imagery, researchers can derive accurate and detailed estimates of these important variables, providing valuable information for optimizing management practices and understanding rice crops dynamics. The model of biomass and nitrogen in rice is an important research area in food security, as rice is a staple crop that feeds more than half of the world’s population. Accurately estimating biomass and nitrogen content in rice can help optimize crop management practices, increase yields and reduce environmental impact. The development of this estimation through non-invasive methods, such as the estimation of parameters through multispectral images, allows the optimization of estimation times and crop monitoring. Different Machine Learning (ML) techniques have been implemented in order to correlate Vegetation Indices (VIs) with biomass and nitrogen. These techniques include, linear and nonlinear multivariate regression, Support Vector Machines SVM and NN Neural Networks, along with less explored ones such as Regression Trees TR, Regression Ensembles ER, and Gaussian Regression Processes GPR. This work explores the estimation of biomass and nitrogen in 59 rice plots by means of multispectral images captured at 20 meters height, the experiment aims to 1) Characterize the parameters of biomass and nitrogen in different genotypes of rice crops in Tolima based on the Omicas ecosystem databases; 2) Implement different estimation algorithms with the data obtained from unmanned aerial vehicle imagery of Tolima crops, and evaluate them using regression metrics; 3) Design an estimation model for the behavior of biomass and nitrogen in the study crops that integrates the parameters of vegetative, reproductive, and maturation phenological stages; and 4) Assess the performance of the biomass and nitrogen estimation model through the calculation of regression metrics obtained from the comparison between traditional measurement methods and image processing.Magíster en Ingeniería ElectrónicaMaestría0000-0001-8451-1833000012174

Rice-yield prediction with multi-temporal sentinel-2 data and 3D CNN: A case study in Nepal

Crop yield estimation is a major issue of crop monitoring which remains particularly challenging in developing countries due to the problem of timely and adequate data availability. Whereas traditional agricultural systems mainly rely on scarce ground-survey data, freely available multi-temporal and multi-spectral remote sensing images are excellent tools to support these vulnerable systems by accurately monitoring and estimating crop yields before harvest. In this context, we introduce the use of Sentinel-2 (S2) imagery, with a medium spatial, spectral and temporal resolutions, to estimate rice crop yields in Nepal as a case study. Firstly, we build a new large-scale rice crop database (RicePAL) composed by multi-temporal S2 and climate/soil data from the Terai districts of Nepal. Secondly, we propose a novel 3D Convolutional Neural Network (CNN) adapted to these intrinsic data constraints for the accurate rice crop yield estimation. Thirdly, we study the effect of considering different temporal, climate and soil data configurations in terms of the performance achieved by the proposed approach and several state-of-the-art regression and CNN-based yield estimation methods. The extensive experiments conducted in this work demonstrate the suitability of the proposed CNN-based framework for rice crop yield estimation in the developing country of Nepal using S2 data

A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction

An early and reliable estimation of crop yield is essential in quantitative and financial evaluation at the field level for determining strategic plans in agricultural commodities for import-export policies and doubling farmer’s incomes. Crop yield predictions are carried out to estimate higher crop yield through the use of machine learning algorithms which are one of the challenging issues in the agricultural sector. Due to this developing significance of crop yield prediction, this article provides an exhaustive review on the use of machine learning algorithms to predict crop yield with special emphasis on palm oil yield prediction. Initially, the current status of palm oil yield around the world is presented, along with a brief discussion on the overview of widely used features and prediction algorithms. Then, the critical evaluation of the state-of-the-art machine learning-based crop yield prediction, machine learning application in the palm oil industry and comparative analysis of related studies are presented. Consequently, a detailed study of the advantages and difficulties related to machine learning-based crop yield prediction and proper identification of current and future challenges to the agricultural industry is presented. The potential solutions are additionally prescribed in order to alleviate existing problems in crop yield prediction. Since one of the major objectives of this study is to explore the future perspectives of machine learning-based palm oil yield prediction, the areas including application of remote sensing, plant’s growth and disease recognition, mapping and tree counting, optimum features and algorithms have been broadly discussed. Finally, a prospective architecture of machine learning-based palm oil yield prediction has been proposed based on the critical evaluation of existing related studies. This technology will fulfill its promise by performing new research challenges in the analysis of crop yield prediction and the development