EVALUATING THE PERFORMANCE OF A SEMI-AUTOMATIC APPLE FRUIT DETECTION IN A HIGH-DENSITY ORCHARD SYSTEM USING LOW-COST DIGITAL RGB IMAGING SENSOR

This study investigates the potential use of close-range and low-cost terrestrial RGB imaging sensor for fruit detection in a high-density apple orchard of Fuji Suprema apple fruits (Malus domestica Borkh). The study area is a typical orchard located in a small holder farm in Santa Catarina’s Southern plateau (Brazil). Small holder farms in that state are responsible for more than 50% of Brazil’s apple fruit production. Traditional digital image processing approaches such as RGB color space conversion (e.g., rgb, HSV, CIE L*a*b*, OHTA[I1 , I2 , I3 ]) were applied over several terrestrial RGB images to highlight information presented in the original dataset. Band combinations (e.g., rgb-r, HSV-h, Lab-a, I”2 , I”3 ) were also generated as additional parameters (C1, C2 and C3) for the fruit detection. After, optimal image binarization and segmentation, parameters were chosen to detect the fruits efficiently and the results were compared to both visual and in-situ fruit counting. Results show that some bands and combinations allowed hits above 75%, of which the following variables stood out as good predictors: rgb-r, Lab-a, I”2 , I”3 , and the combinations C2 and C3. The best band combination resulted from the use of Lab-a band and have identical results of commission, omission, and accuracy, being 5%, 25% and 75%, respectively. Fruit detection rate for Lab-a showed a 0.73 coefficient of determination (R2 ), and fruit recognition accuracy rate showed 0.96 R2 . The proposed approach provides results with great applicability for small holder farms and may support local harvest prediction

ATSS Deep Learning-Based Approach to Detect Apple Fruits

In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. Specifically, in fruit detection problems, several recent works were developed using Deep Learning (DL) methods applied in images acquired in different acquisition levels. However, the increasing use of anti-hail plastic net cover in commercial orchards highlights the importance of terrestrial remote sensing systems. Apples are one of the most highly-challenging fruits to be detected in images, mainly because of the target occlusion problem occurrence. Additionally, the introduction of high-density apple tree orchards makes the identification of single fruits a real challenge. To support farmers to detect apple fruits efficiently, this paper presents an approach based on the Adaptive Training Sample Selection (ATSS) deep learning method applied to close-range and low-cost terrestrial RGB images. The correct identification supports apple production forecasting and gives local producers a better idea of forthcoming management practices. The main advantage of the ATSS method is that only the center point of the objects is labeled, which is much more practicable and realistic than bounding-box annotations in heavily dense fruit orchards. Additionally, we evaluated other object detection methods such as RetinaNet, Libra Regions with Convolutional Neural Network (R-CNN), Cascade R-CNN, Faster R-CNN, Feature Selective Anchor-Free (FSAF), and High-Resolution Network (HRNet). The study area is a highly-dense apple orchard consisting of Fuji Suprema apple fruits (Malus domestica Borkh) located in a smallholder farm in the state of Santa Catarina (southern Brazil). A total of 398 terrestrial images were taken nearly perpendicularly in front of the trees by a professional camera, assuring both a good vertical coverage of the apple trees in terms of heights and overlapping between picture frames. After, the high-resolution RGB images were divided into several patches for helping the detection of small and/or occluded apples. A total of 3119, 840, and 2010 patches were used for training, validation, and testing, respectively. Moreover, the proposed method’s generalization capability was assessed by applying simulated image corruptions to the test set images with different severity levels, including noise, blurs, weather, and digital processing. Experiments were also conducted by varying the bounding box size (80, 100, 120, 140, 160, and 180 pixels) in the image original for the proposed approach. Our results showed that the ATSS-based method slightly outperformed all other deep learning methods, between 2.4% and 0.3%. Also, we verified that the best result was obtained with a bounding box size of 160 × 160 pixels. The proposed method was robust regarding most of the corruption, except for snow, frost, and fog weather conditions. Finally, a benchmark of the reported dataset is also generated and publicly available

Contribuições na detecção de frutos de maçã em pomar de alta densidade utilizando imagens obtidas para fotogrametria terrestre

Orientador: Prof. Dr. Edson Aparecido MitishitaTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências da Terra, Programa de Pós-Graduação em Ciências Geodésicas. Defesa : Curitiba, 29/07/2021Inclui referências: p. 94-103Resumo: A maleicultura brasileira faz uso de operações manuais para fazer avaliação de produção de pomar. Essas operações são morosas, despendem de esforço físico e necessitam no mínimo dois operadores para serem realizadas. Partindo desse problema propôs-se utilizar técnicas de Visão Computacionais e de Fotogrametria para obtenção de informações quantitativas e individualizadas de plantas de um pomar comercial de maçã. O estudo foi realizado em um pomar no município de Correia Pinto-SC, a cultivar analisada foi a Fuji Suprema, próximo ao período da colheita. Foram analisadas 8 linhas, identificadas de L1 a L8 com 10 plantas cada. A coleta de dados dividiu-se em duas etapas: 1) A Primeira etapa foi a aquisição das imagens que permitiram as análises realizadas por Fotogrametria; nesta etapa foram fotografadas 8 linhas em ambos os lados, e com sobreposição entre as fotos. 2) a Segunda foi a aquisição dos dados do pomar de modo convencional onde foram coletados: Diâmetro do Caule (Di), Altura da planta (H), Comprimento (Co), Largura (Lg), Número dos Frutos (NF) e Peso Médio do Fruto (PMF). Duas metodologias (A) e (B) de detecção de frutos de maçã foram desenvolvidas, empregando de forma independente técnicas de Processamento Digital de Imagem (PDI) e de Aprendizado Profundo - Deep Learning (DL). A metodologia (A) usou técnica do espaço de cores para detectar os frutos da maçã em uma Linha, com 20 imagens. Como avaliação calculou-se valores de comissão, omissão e acurácia para as detecções dos frutos. As bandas que apresentaram melhor desempenho foram Lab-a, Combinações C2 e C3, com valores aproximados de 0,06, 0,25 e 0,76 para comissão, omissão e acurácia, respectivamente. Na contagem de frutos proposta Lab-a apresentou um R2 de 0,73 e na comparação com o acerto na posição dos frutos o R2 foi de 0,96. A metodologia (B) explorou sete modelos diferentes de DL para realizar a detecção dos frutos. Os modelos de DL avaliados foram ATSS, Faster-RCNN, Libra-RCNN, Cascade-RCNN, RetinaNet, FSAF e HRNet. Das experimentações realizadas conclui-se que os melhores resultados foram obtidos com ATSS, HRNet e FSAF, resultado Precisões Médias de 0,925, 0,922 e 0,922, respectivamente. Empregando o modelo ATSS foram efetuadas experimentações visando explorar diferentes tamanhos de caixas delimitadoras (BB), diferentes níveis de densidade de frutos por patches, e corrupção da imagem. Os melhores resultados foram: na BB o tamanho de 160 x 160 pixel, na densidade de frutos o valor de (20-29) frutos, e nas imagens corrompidas o método teve bom desempenho exceto para condições simuladas de neve, geada e névoa.Abstract: The Brazilian apple crop has used manual operations to evaluate its orchard production. These operations are time-consuming, require physical effort, and need at least two operators to be performed. Considering these difficulties, a study was performed to develop semi-automatic approaches to obtain quantitative and individualized information of plants from a commercial apple orchard using Computer Vision and Photogrammetry techniques. The study area was carried out in an orchard at Correia Pinto-SC; Fuji Suprema was the cultivar analyzed near to the harvest period. Eight rows with ten apple plants in each row, identified as L1 to L8, were analyzed. Data collection was divided into two steps: 1) The first step was the acquisition of images that allowed the analysis performed by Photogrammetry; In this step, 8 rows were photographed on both sides. A large overlap between the photos was used, resulting 20 images in each line. 2) In the second step, the basic dataset from the orchard was acquired using a conventional procedure; the following data were collected: Trunk Diameter (Di), Plant Height (H), Length (Co), Width (Lg), Number of Fruits (NF) and Weight Fruit Medium (FMP). Two methodologies (A) and (B) for apple fruit detection were developed using Digital Image Processing (PDI) and Deep Learning (DL) techniques. Methodology (A) used the color space technique to detect apple fruits in a line. Commission, omission, and accuracy were the parameters' values calculated to measure the performance of the approaches used for apple detection. The bands that showed the best performance were Lab-a, Combinations C2, and C3; The obtained results from Lab-a were values near 0.06, 0.25, and 0.76 for commission, omission, and accuracy, respectively. Additionally, the obtained value of R2 was 0.73 and in the comparison with the correct position of the fruits, the R2 was 0.96. Methodology (B) explored seven different DL models to perform the process of fruit detection. The DL models evaluated were ATSS, Faster-RCNN, Libra-RCNN, Cascade-RCNN, RetinaNet, FSAF, and HRNet. From the experiments carried out, it can be concluded that the best results were obtained with ATSS, HRNet, and FSAF, resulting in Average Precision of 0.925, 0.922, and 0.922, respectively. Using the ATSS model, experiments were carried out to explore different sizes of bounding boxes (BB), different levels of fruit density by patches, and image corruption. The best results were: in BB the size of 160 x 160 pixels, in the density of fruits the value of (20- 29) fruits, and in the corrupted images the method performed well except for simulated conditions of snow, frost, and fog

Aplicação de um modelo linear local na determinação de alturas ortométricas referidas ao sistema geodésico brasileiro

O uso de sistemas de posicionamento global por satélites (GNSS) possibilita obtenção rápida de alturas elipsoidais (h) relativamente precisas. Contudo, nas aplicações de engenharia, é necessário determinar as correspondentes alturas ortométricas (H) referidas ao geóide ou ao Sistema Geodésico Brasileiro (SGB), cujos referenciais são o campo gravitacional terrestre e a maré média no marégrafo localizado em Imbituba (SC), respectivamente. A determinação de H em relação à superfície geoidal exige técnicas gravimétricas ou o usode modelos geopotenciais globais ou regionais. Em relação ao SGB aplica-se nivelamento topográfico clássico, partindo-se de marcos da Rede Altimétrica de Alta Precisão (RAAP) até o local de interesse. Em tais métodos, as operações são usualmente laboriosas, de altos custos e despendem muito tempo. Este artigo apresenta a determinação de H referida ao SGB por meio de um modelo linear de primeira ordem calibrado localmente. A metodologia consistiu em utilizar um conjunto de 17 referências de nível (RNs) da RAAP para calibração do modelo e 7 RNs para sua validação. O modelo foi aplicado na avaliação das alturas de dois conjuntos de pontos aleatórios em uma bacia hidrográfica urbana. O modelo pode ser aplicado em regiões onde não há transiçãode zona UTM.Palavras-chave: GPS, Altura Elipsoidal, Bacia Hidrográfica

COMPORTAMENTO ESPACIAL DAS VARIÁVEIS PRODUÇÃO, VOLUME DE COPA E DIÂMETRO DE CAULE DA VARIEDADE MAXI GALA COM A UTILIZAÇÃO DA TÉCNICA DE COKRIGAGEM SOBRE POMAR COMERCIAL EM VACARIA-RS

A Agricultura de Precisão (AP) permite a utilização de diferentes ferramentas na obtenção de informações, e estas otimizam a tomada de decisão por parte do produtor, impactando positivamente na receita final. O objetivo do trabalho foi verificar o comportamento das variáveis produção (PROD), volume de copa (VC) e diâmetro de caule (DC) da variedade Maxi Gala com a utilização da técnica da cokrigagem. O experimento foi conduzido em uma área de 0,90 hectare de produção comercial da variedade Maxi Gala, na Fazenda São Paulino, da empresa RASIP, em Vacaria-RS, apresentando como coordenadas geográficas 28º31’17” de latitude sul e 50º49’17” de longitude oeste, durante as safras de 2010/2011 e 2011/2012. Coletaram-se 75 amostras para cada variável, em uma malha de 12 m na entrelinha e 10 m na linha. As variáveis avaliadas foram produção por planta , volume de copa e diâmetro de caule. Foram feitas a análise estatística descritiva dos dados e a análise espacial através dos semivariogramas. De posse dos modelos ajustados, realizou-se a interpolação pelo método da krigagem. Após, foi realizada a correlação simples dos parâmetros e elaborado os semivariogramas cruzados para interpolação, pela técnica da cokrigagem. Os parâmetros PROD versus DCapresentaram média correlação na variedade Maxi Gala, na safra de 2011. Nas safras de 2011 e 2012, os parâmetros VC versus DC também apresentaram média correlação. A técnica da cokrigagem pode ser uma ferramenta daAP a ser utilizada pela cultura da macieira no levantamento de informações.Verificou-se que houve resposta e redução na coleta de amostras das variáveis mais difíceis; na safra de 2011, reduziu-se a coleta de 15 amostras da PROD, e na safra de 2012, reduziu-se a coleta de 20 amostras do VC

Comparação de modelos digitais de elevação de SRTM e ASTER com modelo de elevação de grande escala do município de Lages - SC

The digital elevation models today are a great help in planning and projects. But get them is very expensive by conventional surveys. There is the willingness EDM prepared using satellite sensors such as ASTER and SRTM. The objective of this study was to analyze the behavior of altimetric SRTM and ASTER products, compared to an EDM data generated from photogrammetry of the urban area of the municipality of Lages, Santa Catarina State, Brazil, in thousand points random sample processed in a GIS. With the base map of the city hall was generated an EDM software and imported the files corresponding to the area analyzed SRTM and ASTER. After thousand points were created by random sampling tool HawthsTools and were used for extracting the altitudes of the three layers of EDM. The data were extracted from altitudes in tables and statistical analysis was performed considering the altitude of the city hall of the EDM as an attestant. It was used the paired T-test to compare samples SRTM and ASTER data with city hall. Visually the EDM of SRTM was more like of the city EDM. Analyzing the values of sampled thousand points of comparison, the data showed 4.945 meters of mean difference between PML and ASTER and 3.345 meters between PML and SRTM. The confidence interval for the difference showed no significant differences between PML-SRTM for altimetric accuracies between 2.65 and 4.035 meters and PML-ASTER no difference in accuracies between 4.23 and 5.66 meters. The data comparison showed that the EDM has values closer to cartographic data base of the municipality of Lages, in relation to the values of the EDM of ASTER for isolated points sampled.Pages: 4647-465

Benefits of Combining ALOS/PALSAR-2 and Sentinel-2A Data in the Classification of Land Cover Classes in the Santa Catarina Southern Plateau

The Santa Catarina Southern Plateau is located in Southern Brazil and is a region that has gained considerable attention due to the rapid conversion of the typical landscape of natural grasslands and wetlands into agriculture, reforestation, pasture, and more recently, wind farms. This study’s main goal was to characterize the polarimetric attributes of the experimental quad-polarization acquisition mode of the Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR-2) for mapping seven land cover classes. The polarimetric attributes were evaluated alone and combined with SENTINEL-2A using a supervised classification method based on the Support Vector Machine (SVM) algorithm. The results showed that the intensity backscattering alone reached an overall classification accuracy of 37.48% and a Kappa index of 0.26. Interestingly, the addition of polarimetric features increased to 71.35% and 0.66, respectively. It shows that the use of polarimetric decomposition features was relatively efficient in discriminating land cover classes. SENTINEL-2A data alone performed better and achieved a weighted overall accuracy and Kappa index of 85.56% and 0.82. This increase was also significant for the Z-test. However, the addition of ALOS/PALSAR-2 derived features to SENTINEL-2A slightly improved accuracy and was marginally significant at a 95% confidence level only when all features were considered. Possible implications for that performance are the accumulated precipitation prior to SAR data acquisition, which coincides with the rainy season period. The experimental quad-polarization mode of ALOS/PALSAR- 2 shall be evaluated in the near future over different seasonal conditions to confirm results. Alternatively, further studies are then suggested by focusing on additional features derived from SAR data such as texture and interferometric coherence to increase classification accuracy. These measures would be an interesting data source for monitoring specific land cover classes such as the threatened grasslands and wetlands during periods of frequent cloud coverage. Future investigations could also address multitemporal approaches employing either single or multifrequency SAR