Hybrid Neural Networks with Attention-based Multiple Instance Learning for Improved Grain Identification and Grain Yield Predictions

Agriculture is a critical part of the world's food production, being a vital aspect of all societies. Procedures need to be adjusted to their specific environment because of their climate and field condition disparity. Existing research has demonstrated the potential of grain yield predictions on Norwegian farms. However, this research is limited to regional analytics, which is unable to acquire sufficient plant growth factors influenced by field conditions and farmers' decisions. One factor critical for yield prediction is the crop type planted on a per-field basis. This research effort proposes a novel approach for improving crop yield predictions using a hybrid deep neural network utilizing temporal satellite imagery from a remote sensing system. Additionally, We apply a variety of data, including grain production, meteorological data, and geographical data. The crop yield prediction system is supported by a field-based crop type classification model, which supplies features related to crop type and field area. Our crop classification system takes advantage of both raw satellite images as well as carefully chosen vegetation indices. Further, we propose a multi-class attention-based deep multiple instance learning model to utilize semi-labeled datasets, fully benefiting Norwegian data acquisition. Our best crop classification model, which consists of a time distributed network and a gated recurrent unit, classifies crop types with an accuracy of 70\% and is currently state-of-the-art for country-wide crop type mapping in Norway. Lastly, our yield prediction system enables realistic in-season early predictions that could benefit actors in real-life scenarios

Data-Driven Air Quality and Environmental Evaluation for Cattle Farms

The expansion of agricultural practices and the raising of animals are key contributors to air pollution. Cattle farms contain hazardous gases, so we developed a cattle farm air pollution analyzer to count the number of cattle and provide comprehensive statistics on different air pollutant concentrations based on severity over various time periods. The modeling was performed in two parts: the first stage focused on object detection using satellite data of farm images to identify and count the number of cattle; the second stage predicted the next hour air pollutant concentration of the seven cattle farm air pollutants considered. The output from the second stage was then visualized based on severity, and analytics were performed on the historical data. The visualization illustrates the relationship between cattle count and air pollutants, an important factor for analyzing the pollutant concentration trend. We proposed the models Detectron2, YOLOv4, RetinaNet, and YOLOv5 for the first stage, and LSTM (single/multi lag), CNN-LSTM, and Bi-LSTM for the second stage. YOLOv5 performed best in stage one with an average precision of 0.916 and recall of 0.912, with the average precision and recall for all models being above 0.87. For stage two, CNN-LSTM performed well with an MAE of 3.511 and an MAPE of 0.016, while a stacked model had an MAE of 5.010 and an MAPE of 0.023

Farm Area Segmentation in Satellite Images Using DeepLabv3+ Neural Networks

Farm detection using low resolution satellite images is an important part of digital agriculture applications such as crop yield monitoring. However, it has not received enough attention compared to high-resolution images. Although high resolution images are more efficient for detection of land cover components, the analysis of low-resolution images are yet important due to the low-resolution repositories of the past satellite images used for timeseries analysis, free availability and economic concerns. In this paper, semantic segmentation of farm areas is addressed using low resolution satellite images. The segmentation is performed in two stages; First, local patches or Regions of Interest (ROI) that include farm areas are detected. Next, deep semantic segmentation strategies are employed to detect the farm pixels. For patch classification, two previously developed local patch classification strategies are employed; a two-step semi-supervised methodology using hand-crafted features and Support Vector Machine (SVM) modelling and transfer learning using the pretrained Convolutional Neural Networks (CNNs). For the latter, the high-level features learnt from the massive filter banks of deep Visual Geometry Group Network (VGG-16) are utilized. After classifying the image patches that contain farm areas, the DeepLabv3+ model is used for semantic segmentation of farm pixels. Four different pretrained networks, resnet18, resnet50, resnet101 and mobilenetv2, are used to transfer their learnt features for the new farm segmentation problem. The first step results show the superiority of the transfer learning compared to hand-crafted features for classification of patches. The second step results show that the model trained based on resnet50 achieved the highest semantic segmentation accuracy.acceptedVersionPeer reviewe

Farm Detection based on Deep Convolutional Neural Nets and Semi-supervised Green Texture Detection using VIS-NIR Satellite Image

Farm detection using low resolution satellite images is an important topic in digital agriculture. However, it has not received enough attention compared to high-resolution images. Although high resolution images are more efficient for detection of land cover components, the analysis of low-resolution images are yet important due to the low-resolution repositories of the past satellite images used for timeseries analysis, free availability and economic concerns. The current paper addresses the problem of farm detection using low resolution satellite images. In digital agriculture, farm detection has significant role for key applications such as crop yield monitoring. Two main categories of object detection strategies are studied and compared in this paper; First, a two-step semi-supervised methodology is developed using traditional manual feature extraction and modelling techniques; the developed methodology uses the Normalized Difference Moisture Index (NDMI), Grey Level Co-occurrence Matrix (GLCM), 2-D Discrete Cosine Transform (DCT) and morphological features and Support Vector Machine (SVM) for classifier modelling. In the second strategy, high-level features learnt from the massive filter banks of deep Convolutional Neural Networks (CNNs) are utilised. Transfer learning strategies are employed for pretrained Visual Geometry Group Network (VGG-16) networks. Results show the superiority of the high-level features for classification of farm regions.publishedVersionPeer reviewe

Spatial and Temporal Analysis of the Land Use and Land Cover Changes in Gatumba Mining Landscape, Rwanda

Mining activities are one of the main driving forces of land use and land cover changes. While opencast mining is continuously carried out, land cover change assessment is of paramount importance for sustainable land resource management and use as a tool for policymakers to understand the mininginduced changes and their response to water flow, forest, and soil in a mining landscape. In Gatumba Region of the Western part of Rwanda, mining activities are still inadequately undertaken and the surrounding environment is affected by artisanal and small scale mining practices. Soil erosion, barren waste rock dumps, and polluted rivers reflect the lack of implementation of proper environmental protection measures. This study aims to spatially and temporally analyse the land use and cover changes induced by the mining activities in Gatumba mining landscape for optimization of land use planning and management of the mined and restoring the degraded mining landscapes in Rwanda. Landsat images were used to generate the land use/cover maps for the periods of 1999, 2008 and 2015 by using maximum likelihood pixel-based classification method. The comparison of land use/land cover maps during those periods derived from toposheet and multispectral satellite imagery interpretation indicates that there is a significant increase in bare soil, built-up areas at the rate of 0.63% and 45.43% respectively. Over a period of 16 years, mining areas has increased progressively from 2.85 Km2 to 5.55 Km2representing 3.95% of the total land coverage. The expansion has taken place especially in central and southern part of Gatumba. It is also noted that substantial amount of the agriculture land and forest area vanished during the period of study which may be due to rapid population growth and the development of rural centers. The information obtained from change detection of land use/land cover helps in providing optimal solutions for land management, planning for the mining sites, monitoring of the related environmental effects and restoration of the degraded mining landscapes in Rwanda

Spatiotemporal analysis of gapfilled high spatial resolution time series for crop monitoring.

[ES] La obtención de mapas fiables de clasificación de cultivos es importante para muchas aplicaciones agrícolas, como el monitoreo de los campos y la seguridad alimentaria. Hoy en día existen distintas bases de datos de cobertura terrestre con diferentes escalas espaciales y temporales cubriendo diferentes regiones terrestres (por ejemplo, Corine Land cover (CORINE) en Europa o Cropland Data Layer (CDL) en Estados Unidos (EE.UU.)). Sin embargo, estas bases de datos son mapas históricos y por lo tanto no reflejan los estados fenológicos actuales de los cultivos. Normalmente estos mapas requieren un tiempo específico (anual) para generarse basándose en las diferentes fenologías de cada cultivo. Los objetivos de este trabajo son dos: 1- analizar la distribución espacial de los cultivos a diferentes regiones espaciales para identificar las áreas más representativas. 2- analizar el rango temporal utilizado para acelerar la generación de mapas de clasificación. El análisis se realiza sobre el contiguo de Estados Unidos (CONUS, de sus siglas en inglés) en 2019. Para abordar estos objetivos, se utilizan diferentes fuentes de datos. La capa CDL, una base de datos robusta y completa de mapas de cultivo en el CONUS, que proporciona datos anuales de cobertura terrestre rasterizados y georeferenciados. Así como, datos multiespectrales a 30 metros de resolución espacial, preprocesados para rellenar los posibles huecos debido a la presencia de nubes y aerosoles en los datos. Este conjunto de datos ha sido generado por la fusión de sensores Landsat y Moderate Resolution Imaging Spectroradiometer (MODIS). Para procesar tal elevada cantidad de datos se utilizará Google Earth Engine (GEE), que es una aplicación que procesa la información en la nube y está especializada en el procesamiento geoespacial. GEE se puede utilizar para obtener mapas de cultivos a nivel mundial, pero requiere algoritmos eficientes. En este estudio se analizarán diferentes algoritmos de aprendizaje de máquina (machine learning) para analizar la posible aceleración de la obtención de los mapas de clasificación de cultivo. En GEE hay diferentes tipos de algoritmos de clasificación disponibles, desde simples árboles de decisión (decision trees) hasta algoritmos más complejos como máquinas de vectores soporte (SVM) o redes neuronales (neural networks). Este estudio presentará los primeros resultados para la generación de mapas de clasificación de cultivos utilizando la menor cantidad posible de información, a nivel temporal, con una resolución espacial de 30 metros.[EN] Reliable crop classification maps are important for many agricultural applications, such as field monitoring and food security. Nowadays there are already several crop cover databases at different scales and temporal resolutions for different parts of the world (e. g. Corine Land cover in Europe (CORINE) or Cropland Data Layer (CDL) in the United States (US)). However, these databases are historical crop cover maps and hence do not reflect the actual crops on the ground. Usually, these maps require a specific time (annually) to be generated based on the diversity of the different crop phenologies. The aims of this work are two: 1- analyzing the multi-scale spatial crop distribution to identify the most representative areas. 2- analyzing the temporal range used to generate crop cover maps to build maps promptly. The analysis is done over the contiguous US (CONUS) in 2019. To address these objectives, different types of data are used. The CDL, a robust and complete cropland mapping in the CONUS, which provides annual land cover data raster geo-referenced. And, multispectral high-resolution gap-filled data at 30 meter spatial resolution used to avoid the presence of clouds and aerosols in the data. This dataset has been generated by the fusion of Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS). To process this large amount of data will be used Google Earth Engine (GEE) which is a cloud-based application specialized in geospatial processing. GEE can be used to map crops globally, but it requires efficient algorithms. In this study, different machine learning algorithms will be analyzed to generate the promptest classification crop maps. Several options are available in GEE from simple decision trees to more complex algorithms like support vector machines or neural networks. This study will present the first results and the potential to generate crop classification maps using as less possible temporal range information at 30 meters spatial resolution.Rajadel Lambistos, C. (2020). Análisis espaciotemporal de series temporales sin huecos de alta resolución espacial. Universitat Politècnica de València. http://hdl.handle.net/10251/155879TFG

Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy

With the advent of agriculture 3.0 and 4.0, researchers are increasingly focusing on the development of innovative smart farming and precision agriculture technologies by introducing automation and robotics into the agricultural processes. Autonomous agricultural field machines have been gaining significant attention from farmers and industries to reduce costs, human workload, and required resources. Nevertheless, achieving sufficient autonomous navigation capabilities requires the simultaneous cooperation of different processes; localization, mapping, and path planning are just some of the steps that aim at providing to the machine the right set of skills to operate in semi-structured and unstructured environments. In this context, this study presents a low-cost local motion planner for autonomous navigation in vineyards based only on an RGB-D camera, low range hardware, and a dual layer control algorithm. The first algorithm exploits the disparity map and its depth representation to generate a proportional control for the robotic platform. Concurrently, a second back-up algorithm, based on representations learning and resilient to illumination variations, can take control of the machine in case of a momentaneous failure of the first block. Moreover, due to the double nature of the system, after initial training of the deep learning model with an initial dataset, the strict synergy between the two algorithms opens the possibility of exploiting new automatically labeled data, coming from the field, to extend the existing model knowledge. The machine learning algorithm has been trained and tested, using transfer learning, with acquired images during different field surveys in the North region of Italy and then optimized for on-device inference with model pruning and quantization. Finally, the overall system has been validated with a customized robot platform in the relevant environment

Land Use And Land Cover Classification And Change Detection Using Naip Imagery From 2009 To 2014: Table Rock Lake Region, Missouri

Land use and land cover (LULC) of Table Rock Lake (TRL) region has changed over the last half century after the construction of Table Rock Dam in 1959. This study uses one meter spatial resolution imagery to classify and detect the change of LULC of three typical waterside TRL regions. The main objectives are to provide an efficient and reliable classification workflow for regional level NAIP aerial imagery and identify the dynamic patterns for study areas. Seven class types are extracted by optimal classification results from year 2009, 2010, 2012 and 2014 of Table Rock Village, Kimberling City and Indian Point. Pixel-based post-classification comparison generated from-to” confusion matrices showing the detailed change patterns. I conclude that object-based random trees achieve the highest overall accuracy and kappa value, compared with the other six classification approaches, and is efficient to make a LULC classification map. Major change patterns are that vegetation, including trees and grass, increased during the last five years period while residential extension and urbanization process is not obvious to indicate high economic development in the TRL region. By adding auxiliary spatial information and object-based post-classification techniques, an improved classification procedure can be utilized for LULC change detection projects at the region level

Extraction of Information from Multispectral and PAN of Landsat Image for Land Use Classification in the Case of Sodozuria Woreda, Wolaita Sodo, Ethiopia

High-resolution and multispectral remote sensing images are an important data source for acquiring geospatial information for a variety of applications. The satellite images at different spectral and spatial resolutions with the aid of image processing techniques can improve the quality of information. More specifically, image fusion is very helpful to extract the spatial information from two images of different spatial and spectral images of same area. The Image fusion techniques are also helpful in providing classification accurately. In order to improve the information contents of the remote sensing satellite images at a specific spatial resolution, the different resolution image fusion techniques like Wavelet, PC and IHS have been used to combine panchromatic and multispectral datasets of Landsat ETM+ for the purpose of information extraction. The image under study has been used to identify existing Land use types and perform supervised classification. It has then been identified that forest land, farm land, bare land and built-up area are the most dominant land uses in the study area. Based on the supervised classification, classification accuracy assessment has indicated that Original image (MS) produced 83.33% overall accuracy and 0.7500 Kappa coefficient, PC fused image produced 91.67% overall accuracy and 0.875 Kappa coefficient, IHS fused image produced 86.67% overall accuracy and 0.800 Kappa coefficient, Wavelet-PC based transformation produced 91.67% overall accuracy  and   0.875 Kappa coefficient and Wavelet-HIS based  transformation produced 98.33% overall accuracy and 0.975 Kappa coefficient. Moreover, Wavelet-HIS based transformation method has produced relatively higher accuracy. Generally, based on the overall accuracy and kappa coefficient, fused images in terms of classification accuracy at the expense of information content perform by far better than the original image.