ANALISIS PERFORMANSI IMAGE REGISTRATION PAIR MODE BERBASIS SPARSE REPRESENTATION

Pengukuran kesamaan atau similarity measure adalah hal penting dalam image registration. Dalam penelitian kali ini penulis mengukur kesamaan dari dua buah gambar yang salah satunya sudah diregistrasikan dimana gambar pertama menjadi groundtruth. Pengukuran kesamaan telah banyak diteliti sebelumnya dengan banyak metode dengan hasil yang baik, tetapi masih ditemukan beberapa celah dimana pengukuran kesamaan tidak bisa diterapkan di semua kondisi. Sparse Representation (SR) adalah salah satu metode dalam pengukuran kesamaan di image registration dimana metode ini menghitung melalui indeks sparsness dari gambar. Keunggulan dari metode SR ini adalah akurasi dari kemiripan/kesamaan dari gambar masukan yang bisa terhitung dengan baik. Metode SR ini juga cukup kuat dalam menangani gambar dalam intensitas distorsi yang besar, yang banyak terdapat dalam gambar medis, gambar jarak yang jauh yang disebabkan perbedaan modalitas akuisisi dan kondisi iluminasi. Hasil yang didapatkan dalam penelitian ini antara lain nilai Root Mean Square Error (RMSE) dengan nilai terbaik sebesar 39,5825, nilai Peak-Signal to Noise Ratio (PSNR) dengan nilai terbaik 16,18 dB, nilai Structural Similarity Index (SSIM) dengan nilai terbaik 0,8318, nilai Correlation Coefficient (CC) dengan nilai terbaik 0,732, dan nilai Coherence dengan nilai terbaik 0,268

A multi-temporal phenology based classification approach for Crop Monitoring in Kenya

The SBAM (Satellite Based Agricultural Monitoring) project, funded by the Italian Space Agency aims at: developing a validated satellite imagery based method for estimating and updating the agricultural areas in the region of Central-Africa; implementing an automated process chain capable of providing periodical agricultural land cover maps of the area of interest and, possibly, an estimate of the crop yield. The project aims at filling the gap existing in the availability of high spatial resolution maps of the agricultural areas of Kenya. A high spatial resolution land cover map of Central-Eastern Africa including Kenya was compiled in the year 2000 in the framework of the Africover project using Landsat images acquired, mostly, in 1995. We investigated the use of phenological information in supporting the use of remotely sensed images for crop classification and monitoring based on Landsat 8 and, in the near future, Sentinel 2 imagery. Phenological information on crop condition was collected using time series of NDVI (Normalized Difference Vegetation Index) based on Landsat 8 images. Kenyan countryside is mainly characterized by a high number of fragmented small and medium size farmlands that dramatically increase the difficulty in classification; 30 m spatial resolution images are not enough for a proper classification of such areas. So, a pan-sharpening FIHS (Fast Intensity Hue Saturation) technique was implemented to increase image resolution from 30 m to 15 m. Ground test sites were selected, searching for agricultural vegetated areas from which phenological information was extracted. Therefore, the classification of agricultural areas is based on crop phenology, vegetation index behaviour retrieved from a time series of satellite images and on AEZ (Agro Ecological Zones) information made available by FAO (FAO, 1996) for the area of interest. This paper presents the results of the proposed classification procedure in comparison with land cover maps produced in the past years by other projects. The results refer to the Nakuru County and they were validated using field campaigns data. It showed a satisfactory overall accuracy of 92.66 % which is a significant improvement with respect to previous land cover maps

PHENOMICS-DATA DRIVEN TOOLS FOR MACHINE LEARNING-ASSISTED DECISION SUPPORT IN AGRICULTURE

The advancements in sensor technologies – in omics research in agriculture – have increased the amount of generated data. One example of omics data is phenomics, the science of measuring phenotypes (i.e., crop and animal phenotype). Phenomics data are widely used in crop improvement research, precision crop, and livestock farming, allowing rapid, objective, and accurate measurement of phenotypes. In this dissertation, phenomics-data-driven tools were explored and developed to improve and optimize decision support in diverse agricultural applications.Aphanomyces root rot (ARR) disease resistance is an important trait to evaluate in pulse crops. Two image-based disease severity classification approaches were assessed to distinguish between three classes of ARR severity: resistant, intermediate, and susceptible, using Red-Green-Blue images of lentil roots. One approach was through hand-crafted features extracted from the images that were used as an input dataset for a generalized linear model with elastic net regularization. The second approach entailed building a convolutional neural network as an end-to-end feature learning and classification approach. The resistant class was accurately classified with both methods, with an accuracy of 0.92-0.96, while the former approach performed better for the other two classes. In another study, the scalability of field-based remote sensing using high-resolution satellite imagery and the synergy with unmanned aerial systems (UAS) sensing were evaluated to estimate the yield of field pea at the breeding plot level. The assessment was performed using feature fusion (satellite and UAS data) and image fusion (pan-sharpening of satellite data using UAS data). We found that the performance of satellite-imagery data-based random forest models was comparable to UAS-imagery data-based models at a later growth stage (after canopy closure). In the final study, health summary measures were developed to inform dairy cattle’s health status and disease severity. An approach to derive these metrics – objectively and using pathophysiological data – was established to evaluate four common diseases (ketosis, hypocalcemia, metritis, mastitis) in dairy cattle. The approach was found to show potential application in indicating health status based on comparisons with disability weight class indicators reported in the literature

Tecniche di classificazione e monitoraggio delle aree agricole nei paesi dell’Africa Centro-Orientale da immagini satellitari

Il progetto Africover per l’Africa Orientale fu attivo fra gli anni 1995 e 2002, ed ebbe come obiettivo la produzione di un archivio digitale georeferenziato volto alla classificazione delle tipologie di copertura del suolo, nonché alla determinazione della loro destinazione di utilizzo; venne inoltre introdotto un riferimento geodetico omogeneo e stabilita una chiara ed univoca toponimia di strade e bacini idrici presenti nell’area. La FAO e la Cooperazione Italiana supportarono, in risposta a numerose richieste di assistenza a livello sia nazionale che locale, la creazione dell’archivio e la realizzazione delle mappe tematiche di land cover poste a fondazione dei criteri decisionali per la gestione e lo sfruttamento delle risorse naturali delle regioni africane. A partire dal 2002 l’archivio realizzato non è mai più stato aggiornato; da questo punto parte il nostro lavoro, con l’obiettivo di realizzare mappe aggiornate delle aree agricole del Kenya impiegando sensori della classe del Thematic Mapper, sviluppare una metodologia di stima dello stato delle culture per un accurata previsione dei raccolti, sviluppare ed implementare un sistema software che renda agevole la creazione di mappe di uso del suolo da immagini satellitari. Il progetto SBAM (Satellite Based Agricultural Monitoring), risponde alle esigenze di ottenere delle mappe aggiornante ed accurate delle aree dedicate all'agricoltura nei paesi dell'Africa Centrale, fornire alle istituzioni di ricerca locali il know how per perseguire l’attività di aggiornamento del land cover ed attivare un sistema di monitoraggio dello stato di salute delle colture e previsione dei raccolti ed infine potenziare la stazione di telerilevamento del BSC (Broglio Space Center) di Malindi. La procedura di classificazione MDT (Multi-variate Decision Tree) del dato satellitare segue un approccio fenologico multi-temporale, supportato dall’impiego della zonazione agro-ecologica; un sistema automatico di modellizzazione climatico-ambientale interviene a calibrare i parametri di simulazione in ambiente FAO AquaCrop per l’individuazione dei parametri di stress attivi sulle coltivazioni e l’effetto da essi determinato sul livello di produzione atteso. L'area di studio comprende i paesi dell'Africa Centrale ed in particolare i paesi che cadono nel cerchio di acquisizione della stazione di telerilevamento del BSC di Malindi.We investigated the use of phenological information extracted from satellite imagery combined with crop calendar and supported by agro-ecological zoning (AEZ) in accurate crop classification and monitoring. Vegetation indices extracted from Landsat 8 imagery are capable to track the vegetation development through the year and from them the phenological profile can be extrapolated and implemented into a multi-temporal automatic classification process to detect agricultural vegetated areas and to discriminate among different crop species.The classification procedure is supported by the agro-ecological zoning which, based on crop modeling and environmental matching procedures, identifies crop-specific environmental limitations under assumed levels of inputs and management conditions. The Fao AquaCrop simulation model was implemented to estimate the site specific crop yield response to different stress factors. Accurate crop classification and monitoring is the main objective of the SBAM (Satellite Based Agricultural Monitoring) project funded by the Italian Space Agency and focused on Kenya

Ciência de dados na era da agricultura digital: anais.

Estes anais contêm o texto completo dos trabalhos apresentados no XI Congresso Brasileiro de Agroinformática (SBIAgro 2017), o qual foi promovido pela Embrapa Informática Agropecuária e pela Faculdade de Engenharia Agrícola, Instituto de Computação e pelo Centro de Pesquisas Meteorológicas e Climáticas Aplicadas à Agricultura da Universidade Estadual de Campinas (Unicamp). Esta edição do evento foi realizada no Centro de Convenções e na Casa do Lago da Unicamp, localizados na cidade de Campinas (SP). O propósito do evento foi o de reunir pesquisadores, professores, estudantes, empresários e funcionários de empresas para discutir o tema da informática aplicada à agricultura, além de promover um ambiente propício para o surgimento de novos relacionamentos, projetos e negócios.Organizadores: Jayme Garcia Arnal Barbedo, Maria Fernanda Moura, Luciana Alvim Santos Romani, Thiago Teixeira Santos, Débora Pignatari Drucker. SBIAgro 2017