Inteligencia Artificial (IA) como tecnología complementaria a la Teledetección (RS) agrícola en la enseñanza de la fisiología vegetal

Ali Ahmad – Universidad de Granada - 0000-0001-5530-7374Shab E Noor – Universidad de Granada - 0000-0003-0345-4692Pedro Cartujo Cassinello – Universidad de Granada - 0000-0001-6072-3137Vanessa Martos Núñez – Universidad de Granada - 0000-0001-6442-7968Recepción: 28.10.2022 | Aceptado: 31.10.2022Correspondencia a través de ORCID: Ali Ahmad - 0000-0001-5530-7374Financiación: This work was supported by the projects: “VIRTUOUS” funded from the European Union’s Horizon 2020 Project H2020-MSCA-RISE-2019. Ref. 872181, “SUSTAINABLE” funded from the European Union’s Horizon 2020 Project H2020-MSCA-RISE-2020. Ref. 101007702, and the “Project of Excellence” from FEDER (Fondo Europeo de Desarrollo Regional)- Junta de Andalucía 2018. Ref. P18-H0-4700.Área o categoría del conocimiento: Fisiología Vegetal – DocenciaAbstract: Agriculture is facing several challenges such as climate change, drought, and loss of fertile land, which could compromise global food safety and security. In this scenario, integration of novel technologies into agriculture could be the possible solution to address these concerns. There are several modern technology tools that can be integrated into agriculture for this purpose. Agricultural remote sensing (RS) technology, being one of the promising tools, has long been used for agriculture, but its potential has not been explored fully. RS involves monitoring and analysis of various crop growth parameters generating huge datasets. But management and interpretation of RS generated data is a complex and costly process. Therefore, artificial intelligence (AI), another promising tool of 5th industrial era, could be used to complement agricultural RS technology to improve data processing and generating visualizing results. Machine learning, a subset of AI, methods have been efficiently employed for disease detection, yield predictions, and biomass estimations. Yet, there remains a huge possibility to develop crop growth and yield simulations, and machine training models from the freely available satellite data. Hence, indicating and instilling this knowledge into young students would result in the novel initiatives in agricultural plant physiology, since most of the parameters analyzed through RS are physiological.Resumen: La agricultura se enfrenta a varios retos, como el cambio climático, la sequía y la pérdida de tierras fértiles, que podrían comprometer la seguridad alimentaria mundial. En este escenario, la integración de tecnologías novedosas en la agricultura podría ser la posible solución para hacer frente a estos problemas. Existen varias herramientas tecnológicas modernas que pueden integrarse en la agricultura con este fin. La tecnología de teledetección agrícola (RS), que es una de las herramientas más prometedoras, se utiliza desde hace tiempo en la agricultura, pero su potencial no se ha explorado plenamente. La teledetección implica el seguimiento y el análisis de diversos parámetros de crecimiento de los cultivos, lo que genera enormes conjuntos de datos. Pero la gestión e interpretación de los datos generados por la RS es un proceso complejo y costoso. Por lo tanto, la inteligencia artificial (IA), otra herramienta prometedora de la quinta era industrial, podría utilizarse para complementar la tecnología de RS agrícola con el fin de mejorar el procesamiento de los datos y generar resultados de visualización. Los métodos de aprendizaje automático, un subconjunto de la IA, se han empleado eficazmente para la detección de enfermedades, la predicción del rendimiento y la estimación de la biomasa. Sin embargo, sigue existiendo una enorme posibilidad de desarrollar simulaciones de crecimiento y rendimiento de los cultivos, así como modelos de entrenamiento de máquinas a partir de los datos satelitales disponibles de forma gratuita. Por lo tanto, indicar e inculcar estos conocimientos a los jóvenes estudiantes daría lugar a iniciativas novedosas en la fisiología de las plantas agrícolas, ya que la mayoría de los parámetros analizados mediante RS son fisiológicos.Universidad de GranadaEuropean Union’s Horizon 2020 Project H2020-MSCA-RISE-2019. Ref. 872181European Union’s Horizon 2020 Project H2020-MSCA-RISE-2020. Ref. 101007702“Project of Excellence” from FEDER (Fondo Europeo de Desarrollo Regional)- Junta de Andalucía 2018. Ref. P18-H0-470

Predicting the probability that higher profits could be achieved by adopting PA

Algorithms were developed to predict spatial variation of yield and quality within winter wheat crops intended for bread-making. Bayesian networks were used to predict spatial probability maps of yield and quality based on data sources including yield maps, fertiliser applications, soil variables and Sentinel 2 satellite data. Results presented here for five UK fields show that there was a 65% likelihood of achieving a grain protein premium with variable rate nitrogen application compared to 50% with uniform N. Achieving this premium would increase revenues by £150/ha. A similar comparison for five German fields did not demonstrate a higher probability of profit

Prediction of Yield and Lignocellulosic Composition in Energy Cane Using Unmanned Aerial Systems

Crop monitoring and appropriate agricultural management practices of elite germplasm will enhance bioenergy’s efficiency. Unmanned aerial systems (UAS) may be a useful tool for this purpose. The objective of this study was to assess the use of UAS with true color and multispectral imagery to predict the yield and total cellulosic content (TCC) of newly created energy cane germplasm. A trial was established in the growing season of 2016 at the Texas A&M AgriLife Research and Extension Center in Weslaco, Texas, where 15 energy cane elite lines and three checks were grown on experimental plots, arranged in a randomized complete block design (RCBD) and replicated four times. Four flights were executed at different growth stages in 2018, at the first ratoon crop, using two multi-rotor UAS: the DJI Phantom 4 Pro equipped with RGB camera and the DJI Matrice 100, equipped with multispectral sensor (SlantRange 3p). Canopy cover, canopy height, NDVI (Normalized Difference Vegetation Index), and ExG (Excess Green Index) were extracted from the images and used to perform a stepwise regression to obtain the yield and TCC models. The results showed a good agreement between the predicted and the measured yields (R2 = 0.88); however, a low coefficient of determination was found between the predicted and the observed TCC (R2 = 0.30). This study demonstrated the potential application of UAS to estimate energy cane yield with high accuracy, enabling plant breeders to phenotype larger populations and make selections with higher confidence

RS-Net: robust segmentation of green overlapped apples

Fruit detection and segmentation will be essential for future agronomic management, with applications in yield estimation, growth monitoring, intelligent picking, disease detection and etc. In order to more accurately and efficiently realize the recognition and segmentation of apples in natural orchards, a robust segmentation net framework specially developed for fruit production is proposed. This model was improved for the more challenging problem which segments the overlapped apples from the monochromatic background regardless of various corruptions. The method extends Mask R-CNN by embedding an attention mechanism for focusing more on the informative pixels but also suppressing the noise caused by adverse factors (occlusions, overlaps, etc.), which could be more suitable and robust for operating in complex natural environment. Specifically, the Gaussian non-local attention mechanism is transplanted into Mask R-CNN for refining the semantic features generated continuously by residual network and feature pyramid network, then the model forward processing based on the balanced feature levels and finally segments the regions where the apples are located. Experimental results verify the hypothesis of current work and show that the proposed method outperforms other start-of-the-art detection and segmentation models, the AP box and AP mask metric values have reached 85.6% and 86.2% in a reasonable run time, respectively, which can meet the precision and robustness of vision system in agronomic managemen

Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

This work was supported by the projects: "VIRTUOUS" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2019. Ref. 872181, "SUSTAINABLE" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2020. Ref. 101007702 and the "Project of Excellence" from Junta de Andalucia 2020. Ref. P18-H0-4700. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Timely and reliable information about crop management, production, and yield is considered of great utility by stakeholders (e.g., national and international authorities, farmers, commercial units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization (FAO) estimates, around 70% more production of agricultural products will be needed to fulfil the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDGs), especially the second goal of “zero hunger”, potential technologies like remote sensing (RS) need to be efficiently integrated into agriculture. The application of RS is indispensable today for a highly productive and sustainable agriculture. Therefore, the present study draws a general overview of RS technology with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution. Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery, in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs), which is further pushing the boundaries of proficiency through the upgrading of sensors capable of higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies (WST) have streamlined real time information acquisition and programming for respective measures. Improved algorithms and sensors can, not only add significant value to crop data acquisition, but can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by making use of cloud computing. The RS technology generates huge sets of data that necessitate the incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have made the orientation of current research towards the estimation of plant physiological traits rather than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge technologies are discussed in this study. This study can be helpful for researchers, academics, and young students aspiring to play a role in the achievement of sustainable agriculture.European Commission 101007702 872181Junta de Andalucia P18-H0-470

Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle

Leaf area index (LAI) and leaf dry matter (LDM) are important indices of crop growth. Real-time, nondestructive monitoring of crop growth is instructive for the diagnosis of crop growth and prediction of grain yield. Unmanned aerial vehicle (UAV)-based remote sensing is widely used in precision agriculture due to its unique advantages in flexibility and resolution. This study was carried out on wheat trials treated with different nitrogen levels and seeding densities in three regions of Jiangsu Province in 2018–2019. Canopy spectral images were collected by the UAV equipped with a multi-spectral camera during key wheat growth stages. To verify the results of the UAV images, the LAI, LDM, and yield data were obtained by destructive sampling. We extracted the wheat canopy reflectance and selected the best vegetation index for monitoring growth and predicting yield. Simple linear regression (LR), multiple linear regression (MLR), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural network (ANN), and random forest (RF) modeling methods were used to construct a model for wheat yield estimation. The results show that the multi-spectral camera mounted on the multi-rotor UAV has a broad application prospect in crop growth index monitoring and yield estimation. The vegetation index combined with the red edge band and the near-infrared band was significantly correlated with LAI and LDM. Machine learning methods (i.e., PLSR, ANN, and RF) performed better for predicting wheat yield. The RF model constructed by normalized difference vegetation index (NDVI) at the jointing stage, heading stage, flowering stage, and filling stage was the optimal wheat yield estimation model in this study, with an R2 of 0.78 and relative root mean square error (RRMSE) of 0.1030. The results provide a theoretical basis for monitoring crop growth with a multi-rotor UAV platform and explore a technical method for improving the precision of yield estimation

Within-Field Yield Prediction for Sugarcane and Rice Focused on Precision Agriculture Applications

Food and energy security are two main topics when it comes to the on-growing world population. Rice and sugarcane play an important role in this scenario since sugarcane can be used for energy production and rice is one of major staple cereals. In this scenario, Precision Agriculture (PA) management strategies aims to improve productivity, efficiency, profitability, and sustainability, and can help agriculture to fulfill the needs of the growing population in a sustainable way. However, yield maps are essential for PA, but its adoption is still very low. Thus, the main objective of this study was to evaluate the potential of satellite imagery and machine learning to predict yield maps that could support the adoption of precision agriculture practices for rice and sugarcane. Consequently, a framework for the data processing, imagery acquisition and machine learning model generation, was proposed and tested. The results presented a high potential for the usage of those techniques, generating yield maps very similar to the ones obtained from yield monitors (RMSE for rice of 0.9Mg.ha-1 and for sugarcane 3.14Mg.ha-1). Also, in-season yield map prediction was evaluated for rice and sugarcane. Therefore, the prediction was performed for different growth stages by stacking all the images until a specific date. Sugarcane maps were obtained with a satisfactory accuracy early in the season (May-June) (no statistical significance when compared to the predicted maps of the end of the season) whilst for rice the yield maps with the lowest errors were only obtained late in the season. Therefore, sugarcane maps obtained early in the season could be used for in-season management of the crop. On the other hand, the in-season applicability for rice yield maps were limited since accurate maps were obtained at late ripening. However, this information could still be used for harvest planning and nitrogen application on the second harvest of Louisiana’s rice. In general, the framework proposed presented a high potential to be used for yield maps prediction. Furthermore, yield maps, an important tool for PA, were obtained with low errors RMSE of 0.83 and 3.14 Mg.ha-1 for rice and sugarcane, respectively

High Throughput Phenotyping: Field Based Triticale Breeding and Educational Resource Impact

Triticale (Triticosecale) is a multifunctional hybrid cereal crop that adopted the hardiness of rye and wheat\u27s high-yielding and nutritional qualities. Plant breeding programs work to improve the quality and number of varieties available to producers through multiple rounds of evaluation and selection. However, traditional phenotyping methods are labor-intensive, time-consuming, and destructive, creating a phenotyping bottleneck. Remote sensing using unmanned aerial systems has the potential to alleviate this issue and change the evaluation of phenotypes in a breeding. Demand for educational resources to advance public awareness and prepare the workforce has increased with the utilization of more technology in agriculture. Limited research focuses on using UAV-derived vegetation indices to measure biomass in triticale. In addition, agriculture professionals need more education and understanding about the potential benefits and practical implementation of remote sensing technologies in plant breeding contexts. This research aims to (1) Evaluate the potential of UAV-derived vegetation indices to estimate above-ground biomass in triticale and (2) Assess the impact of open educational resources on the change in self-reported and objectively assessed knowledge of phenotype evaluation using high throughput phenotyping. The results of study one show high correlations between biomass and several vegetation indices, indicating that UAV-derived vegetation indices have the potential to be used as an alternative to destructive biomass sampling for phenotyping biomass in triticale. The results of study two show the open educational resource High Throughput Phenotyping in Plant Breeding increases learner overall self-reported knowledge, UAV self-reported knowledge and cross-listed self-reported knowledge. In addition, the lesson increases overall objectively assessed knowledge and cross-listed objectively assessed knowledge. Advisors: Donald Lee and Leah Sandal

Machine Learning-Based Sugarcane Yield Prediction Using Multispectral Time-Series Imagery

Accurate sugarcane yield prediction is important for the sugar industry in serving the demands for decision-making systems such as harvest timing, product handling, and forward sales. Accurate yield modelling offers sugarcane farmers with a deeper knowledge of spatial and temporal crop variability, enhancing the quality and quantity of sugarcane yields while minimizing production costs and alleviating adverse environmental consequences. High-performance Machine Learning (ML) algorithms were applied to Remote Sensing (RS) images so that the timely acquired data with both spatial and temporal resolutions could be processed efficiently to interpret the complexity and variability of sugarcane yield. In this context, we tested advanced ML algorithms on diverse RS datasets such as Unmanned Aerial Vehicle (UAV), Sentinel-2, and Landsat-8 images, validated the results using ground measurements such as wet and dry biomasses, and crop yields; and developed a model that predicts sugarcane crop yield at the earliest possible growth stage with the least amount of spectral data. To demonstrate the scalability of the proposed yield prediction model, its performance was assessed in two regions: an experimental site in Queensland, Australia, and some sugarcane fields in Khuzestan Province, Iran. The predictive model was expanded using freely accessible Sentinel-2 satellite data so that it could be applied to a variety of sugarcane yield studies in various crop systems. For example, the expanded prediction model is particularly useful if ground data collection is limited, or UAV data is not feasible due to surveying costs. This research is anticipated to benefit agricultural producers and farmers in their decision-making and agricultural operation planning and help establish their management practises for optimal productivity

The use of Unmanned Aerial Vehicle based photogrammetric point cloud data for winter wheat intra-field variable retrieval and yield estimation in Southwestern Ontario

Precision agriculture uses high spatial and temporal resolution soil and crop information to control the crop intra-field variability to achieve optimal economic benefit and environmental resources sustainable development. As a new imagery collection platform between airborne and ground measurements, Unmanned Aerial Vehicle (UAV) is used to collect high spatial resolution images at a user selected period for precision agriculture. Most studies extract crop parameters from the UAV-based orthomosaic imagery using spectral methods derived from the satellite and airborne based remote sensing. The new dataset, photogrammetric point cloud data (PCD), generated from the Structure from Motion (SfM) methods using the UAV-based images contains the feature’s structural information, which has not been fully utilized to extract crop’s biophysical information. This thesis explores the potential for the applications of the UAV-based photogrammetric PCD in crop biophysical variable retrieval and in final biomass and yield estimation. First, a new moving cuboid filter is applied to the voxel of UAV-based photogrammetric PCD of winter wheat to eliminate noise points, and the crop height is calculated from the highest and lowest points in each voxel. The results show that the winter wheat height can be estimated from the UAV-based photogrammetric PCD directly with high accuracy. Secondly, a new Simulated Observation of Point Cloud (SOPC) method was designed to obtain the 3D spatial distribution of vegetation and bare ground points and calculate the gap fraction and effective leaf area index (LAIe). It reveals that the ground-based crop biophysical methods are possible to be adopted by the PCD to retrieve LAIe without ground measurements. Finally, the SOPC method derived LAIe maps were applied to the Simple Algorithm for Yield estimation (SAFY) to generate the sub-field biomass and yield maps. The pixel-based biomass and yield maps were generated in this study revealed clearly the intra-field yield variation. This framework using the UAV-based SOPC-LAIe maps and SAFY model could be a simple and low-cost alternative for final yield estimation at the sub-field scale. The results of this thesis show that the UAV-based photogrammetric PCD is an alternative source of data in crop monitoring for precision agriculture