Combining deep learning and X-ray imaging technology to assess tomato seed quality

Traditional germination tests which assess seed quality are costly and time-consuming, mainly when performed on a large scale. In this study, we assessed the efficiency of X-ray imaging analyses in predicting the physiological quality of tomato seeds. A convolutional neural network (CNN) called mask region convolutional neural network (MaskRCNN) was also tested for its precision in adequately classifying tomato seeds into four seed quality categories. For this purpose, X-ray images were taken of seeds of 49 tomato genotypes (46 Solanum pennellii introgression lines) from two different growing seasons. Four replicates of 25 seeds for each genotype were analyzed. These seeds were further assessed for germination and seedling vigor-related traits in two independent trials. Correlation analysis revealed significant linear association between germination and image-based variables. Most genotypes differed in terms of germination and seed development performance considering the two independent trials, except LA 4046, LA 4043, and LA4047, which showed similar behavior. Our findings point out that seeds with low opacity and percentage of damaged seed tissue and high values for living tissue opacity have greater physiological quality. In short, our work confirms the reliability of X-ray imaging and deep learning methodologies in predicting the physiological quality of tomato seeds.

Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview

Some sectors of animal production and reproduction have shown great technological advances due to the development of research areas such as Precision Livestock Farming (PLF). PLF is an innovative approach that allows animals to be monitored, through the adoption of cutting-edge technologies that continuously collect real-time data by combining the use of sensors with advanced algorithms to provide decision tools for farmers. Artificial Intelligence (AI) is a field that merges computer science and large datasets to create expert systems that are able to generate predictions and classifications similarly to human intelligence. In a simplified manner, Machine Learning (ML) is a branch of AI, and can be considered as a broader field that encompasses Deep Learning (DL, a Neural Network formed by at least three layers), generating a hierarchy of subsets formed by AI, ML and DL, respectively. Both ML and DL provide innovative methods for analyzing data, especially beneficial for large datasets commonly found in livestock-related activities. These approaches enable the extraction of valuable insights to address issues related to behavior, health, reproduction, production, and the environment, facilitating informed decision-making. In order to create the referred technologies, studies generally go through five steps involving data processing: acquisition, transferring, storage, analysis and delivery of results. Although the data collection and analysis steps are usually thoroughly reported by the scientific community, a good execution of each step is essential to achieve good and credible results, which impacts the degree of acceptance of the proposed technologies in real life practical circumstances. In this context, the present work aims to describe an overview of the current implementations of ML/DL in livestock reproduction and production, as well to identify potential challenges and critical points in each of the five steps mentioned, which can affect results and application of AI techniques by farmers in practical situations.(AU)

Influence of color and brightness on ontogenetic shelter preference by the prawn Macrobrachium rosenbergii (Decapoda: Palaemonidae)

The giant river prawn, Macrobrachium rosenbergii (de Man, 1879), is a species of great commercial importance. It is farmed under different conditions that translate to a great range of light environments, which impact their behavior and productivity. We present the first study employing both visual modeling and beha vioral data to evaluate the ontogenetic changes in color preferences of juveniles and adults of M. rosenbergii. We offered ten shelters of different colors to juveniles and adults and registered their preferences. Our results show that shelter preference changed with ontogeny: juveniles chose shelters based on chromaticity (preference for blue), while adults based their decisions on brightness (preference for dark grey). This preference adults show for dark colors is probably associated with light avoidance behavior. We recommend providing blue shelters for juveniles and dark shelters for adults.(AU)

Facoemulsificação com implante de lente intraocular em cão / Phacoemulsification technique with dog intraocular lens implant

As afecções oculares são diagnosticadas rotineiramente em clínica de animais domésticos, tornando a oftalmologia um relevante seguimento na Medicina Veterinária. A catarata é caracterizada pela opacidade progressiva docristalino que interfere na absorção de luz que chegará à retina. O objetivo deste trabalho é relatar o caso de tratamento cirúrgico de catarata imatura pela técnica de facoemulsificação com implante de lente intraocular em canino. Um canino, macho, não castrado, da raça poodle, com 14 anos de idade, foi atendido em uma clínica veterinária, na cidade de Manaus, Amazonas. Após diagnóstico, optou-se pela cirurgia através da técnica de facoemulsificação, visando a remoção por meio da fragmentação e aspiração do cristalino, com posterior implante da lente intraocular. Decorridos 30 dias da cirurgia, o paciente retornou para a avalição da evolução do tratamento. Observou-se melhora significativa na visão do animal. Por conseguinte, conclui-se que a técnica foi eficiente no processo de recuperação, de acordo com o progresso significativo dos testes de reflexo, colocação de obstáculos em ambientes escuros e claros.

Eye disorders are routinely diagnosed in domestic animal clinic, which makes ophthalmology a relevant follow up in Veterinary Medicine. Cataracts are characterized by a progressive opacity of the lens that interferes with the absorption of light reaching the retina. The objective of this work is to report the case of surgical treatment of immature cataract using the phacoemulsification technique with intraocular lens implant in canines. A 14-year-oldmale poodle, uncastrated, was seen at a veterinary clinic in the city of Manaus, Amazonas. After diagnosis, surgery was performed using the phacoemulsification technique, aiming at the removal through fragmentation and aspiration of the lens with subsequent implantation of the intraocular lens. After 30 days of the surgery, the patient returned to evaluate the progress of the treatment. A significant improvement in the animal's vision was observed. Therefore, it was concluded that the technique used was an efficient in the recovery process according to significant advances, two reflection tests, placing obstacles in dark and light environments.

Facoemulsificação com implante de lente intraocular em cão / Phacoemulsification technique with dog intraocular lens implant

As afecções oculares são diagnosticadas rotineiramente em clínica de animais domésticos, tornando a oftalmologia um relevante seguimento na Medicina Veterinária. A catarata é caracterizada pela opacidade progressiva docristalino que interfere na absorção de luz que chegará à retina. O objetivo deste trabalho é relatar o caso de tratamento cirúrgico de catarata imatura pela técnica de facoemulsificação com implante de lente intraocular em canino. Um canino, macho, não castrado, da raça poodle, com 14 anos de idade, foi atendido em uma clínica veterinária, na cidade de Manaus, Amazonas. Após diagnóstico, optou-se pela cirurgia através da técnica de facoemulsificação, visando a remoção por meio da fragmentação e aspiração do cristalino, com posterior implante da lente intraocular. Decorridos 30 dias da cirurgia, o paciente retornou para a avalição da evolução do tratamento. Observou-se melhora significativa na visão do animal. Por conseguinte, conclui-se que a técnica foi eficiente no processo de recuperação, de acordo com o progresso significativo dos testes de reflexo, colocação de obstáculos em ambientes escuros e claros.(AU)

Eye disorders are routinely diagnosed in domestic animal clinic, which makes ophthalmology a relevant follow up in Veterinary Medicine. Cataracts are characterized by a progressive opacity of the lens that interferes with the absorption of light reaching the retina. The objective of this work is to report the case of surgical treatment of immature cataract using the phacoemulsification technique with intraocular lens implant in canines. A 14-year-oldmale poodle, uncastrated, was seen at a veterinary clinic in the city of Manaus, Amazonas. After diagnosis, surgery was performed using the phacoemulsification technique, aiming at the removal through fragmentation and aspiration of the lens with subsequent implantation of the intraocular lens. After 30 days of the surgery, the patient returned to evaluate the progress of the treatment. A significant improvement in the animal's vision was observed. Therefore, it was concluded that the technique used was an efficient in the recovery process according to significant advances, two reflection tests, placing obstacles in dark and light environments.(AU)

Computational classification of animals for a highway detection system / Classificação computacional de animais para sistema de detecção nas rodovias

Vehicle-animal collisions represent a serious problem in roadway infrastructure. To avoid these roadway collisions, different mitigation systems have been applied in various regions of the world. In this article, a system for detecting animals on highways is presented using computer vision and machine learning algorithms. The models were trained to classify two groups of animals: capybaras and donkeys. Two variants of the convolutional neural network called Yolo (You only look once) were used, Yolov4 and Yolov4-tiny (a lighter version of the network). The training was carried out using pre-trained models. Detection tests were performed on 147 images. The accuracy results obtained were 84.87% and 79.87% for Yolov4 and Yolov4-tiny, respectively. The proposed system has the potential to improve road safety by reducing or preventing accidents with animals.(AU)

As colisões entre veículos e animais representam um sério problema na infraestrutura rodoviária. Para evitar tais acidentes, medidas mitigatórias têm sido aplicadas em diferentes regiões do mundo. Neste projeto é apresentado um sistema de detecção de animais em rodovias utilizando visão computacional e algoritmo de aprendizado de máquina. Os modelos foram treinados para classificar dois grupos de animais: capivaras e equídeos. Foram utilizadas duas variantes da rede neural convolucional chamada Yolo (você só vê uma vez) ­ Yolov4 e Yolov4-tiny (versão mais leve da rede) ­ e o treinamento foi realizado a partir de modelos pré-treinados. Testes de detecção foram realizados em 147 imagens e os resultados de precisão obtidos foram de 84,87% e 79,87% para Yolov4 e Yolov4-tiny, respectivamente. O sistema proposto tem o potencial de melhorar a segurança rodoviária reduzindo ou prevenindo acidentes com animais.(AU)

Computational classification of animals for a highway detection system / Classificação computacional de animais para sistema de detecção nas rodovias

Vehicle-animal collisions represent a serious problem in roadway infrastructure. To avoid these roadway collisions, different mitigation systems have been applied in various regions of the world. In this article, a system for detecting animals on highways is presented using computer vision and machine learning algorithms. The models were trained to classify two groups of animals: capybaras and donkeys. Two variants of the convolutional neural network called Yolo (You only look once) were used, Yolov4 and Yolov4-tiny (a lighter version of the network). The training was carried out using pre-trained models. Detection tests were performed on 147 images. The accuracy results obtained were 84.87% and 79.87% for Yolov4 and Yolov4-tiny, respectively. The proposed system has the potential to improve road safety by reducing or preventing accidents with animals.(AU)

As colisões entre veículos e animais representam um sério problema na infraestrutura rodoviária. Para evitar tais acidentes, medidas mitigatórias têm sido aplicadas em diferentes regiões do mundo. Neste projeto é apresentado um sistema de detecção de animais em rodovias utilizando visão computacional e algoritmo de aprendizado de máquina. Os modelos foram treinados para classificar dois grupos de animais: capivaras e equídeos. Foram utilizadas duas variantes da rede neural convolucional chamada Yolo (você só vê uma vez) ­ Yolov4 e Yolov4-tiny (versão mais leve da rede) ­ e o treinamento foi realizado a partir de modelos pré-treinados. Testes de detecção foram realizados em 147 imagens e os resultados de precisão obtidos foram de 84,87% e 79,87% para Yolov4 e Yolov4-tiny, respectivamente. O sistema proposto tem o potencial de melhorar a segurança rodoviária reduzindo ou prevenindo acidentes com animais.(AU)

Prediction of Girolando cattle weight by means of body measurements extracted from images

The objective with this study was to analyze the body measurements of Girolando cattle, as well as measurements extracted from their images, to generate a model to understand which measures further explain the cattle body weight. Therefore, the experiment physically measured 34 Girolando cattle (two males and 32 females), for the following traits: heart girth (HGP), circumference of the abdomen, body length, occipito-ischial length, wither height, and hip height. In addition, images of the dorsum and the body lateral area of these animals allowed measurements of hip width (HWI), body length, tail distance to the neck, dorsum area (DAI), dorsum perimeter, wither height, hip height, body lateral area, perimeter of the lateral area, and rib height. The measurements extracted from the images were subjected to the stepwise regression method and regression-based machine learning algorithms. The HGp was the physical measure with stronger positive correlation with respect to body weight. In the stepwise method, the final model generated R² of 0.70 and RMSE of 42.52 kg and the equation. The linear regression and SVM algorithms obtained the best results, followed by discretization regression with random forests. The set of rules presented in this study can be recommended for estimating body weight in Girolando cattle, at a correlation coefficient of 0.71, by measurements of hip width and dorsum area, both extracted from cattle images.(AU)

Mature pomegranate recognition methods in natural environments using machine vision / Métodos de reconhecimento de romã madura em ambientes naturais usando visão de máquina

The use of machine vision to recognize mature pomegranates in natural environments is of major significance in improving the applicability and work efficiency of picking robots. By analyzing the color characteristics of color images of mature pomegranates under different illumination conditions, the feasibility of the YCbCr color model for pomegranate image recognition under different illumination conditions was proven. First, the Cr component map of pomegranate image is selected and then the pomegranate fruit is segmented by the kernel fuzzy C-means clustering algorithm to obtain the pomegranate image. Contrast experiments of pomegranate image segmentation under different illumination conditions were then performed using the proposed kernel fuzzy C-means clustering algorithm, the fuzzy C-means clustering algorithm, the Otsu algorithm and the threshold segmentation algorithm. Results of the experiments verified the effectiveness and superiority of the proposed algorithm.(AU)

O uso de máquina para reconhecer romãs maduras em ambientes naturais é de grande importância para melhorar a aplicabilidade e a eficiência do trabalho de robôs de colheita. Ao analisar as características de cor das imagens coloridas de romãs maduras sob diferentes condições de iluminação, a viabilidade do modelo de cores YCbCr para o reconhecimento de imagens de romãs sob diferentes condições de iluminação foi comprovada. Primeiro, o mapa do componente Cr da imagem da romã é selecionado e, em seguida, o fruto da romãzeira é segmentado pelo algoritmo de agrupamento C-means fuzzy do kernel para obter a imagem da romã. Experimentos contrastados de segmentação de imagens de romã sob diferentes condições de iluminação foram então realizados usando o algoritmo proposto de agrupamento C-means fuzzy, o algoritmo fuzzy de agrupamento C-means, o algoritmo Otsu e o algoritmo de segmentação de limiares. Os resultados dos experimentos verificaram a efetividade e superioridade do algoritmo proposto.(AU)

Vigor-S, a new system for evaluating the physiological potential of maize seeds

The refinement of vigor tests and the possibility of utilizing computer resources for the effective evaluation of the seed physiological potential have attracted considerable interest from research and seed technologists. The aim of this study was to evaluate the physiological potential of maize seeds using the newly-created Automated Analysis of Seed Vigor System (Vigor-S) compared with other recommended seed vigor tests; two maize hybrids were used, each represented by seven seed lots. Germination and vigor (cold test, saturated salt accelerated aging, and field seedling emergence) evaluations were conducted. For the evaluation of seed vigor with the use of seedling image analysis resources, two systems were compared: the Seed Vigor Imaging System (SVIS®), developed by Ohio State University, USA and the Vigor-S, resulting from collaboration between USP/ESALQ and EMBRAPA (Embrapa Instrumentation). Using these two systems, three day old seedlings were scanned and the images were analyzed. Similar results for the vigor index, uniformity of development, and seedling length were obtained. The computerized image analysis of seedlings using Vigor-S has advantages with respect to accuracy, speed, and the possibility of automatic application to a worksheet. It is a consistent alternative for the evaluation of maize seed vigor, and produces information compatible with that obtained by the accelerated aging test and SVIS®.