Peach ripeness classification based on a new one-stage instance segmentation model

Peach instance segmentation is a crucial part to locate peaches and classify their ripeness stages to build an automatic peach harvesting or monitoring machine. This paper proposes a large and high-quality peach dataset called NinePeach, and a new one-stage instance segmentation model. The NinePeach dataset aims to reproduce real-world field conditions, encompassing various factors that can significantly influence the accuracy of peach detection, such as varying natural light intensity, instances of multiple fruit adhesion, and occlusion caused by stems and leaves. This is the largest and the most varied peach dataset among publicly available peach datasets to our best knowledge. Our proposed one-stage segmentation model does not require Region Proposal Network (RPN) to generate bounding box proposals, it directly identifies object instances by their centre locations and sizes and predict their category at the same time. The proposed model incorporates channel attention and spatial attention mechanisms to enhance object detection capabilities in crucial channels and spatial locations. Experimental results show that the state-of-the-art Mask RCNN performs 69.91% average precision (AP) with Swin-T backbone, our model surpasses it with the same backbone, achieving the highest 72.12% AP, and delivering more precise mask and boundary predictions. Specifically, our model is capable of accurately detect peaches under various conditions, such as peaches partially obscured by leaves, peaches partially exposed or overlapped. These advancements present promising prospects for the application of this technology to other fruits or crops

Identifying veraison process of colored wine grapes in field conditions combining deep learning and image analysis

Acknowledgments This work was supported by the National Key R&D Program Project of China (Grant No. 2019YFD1002500) and Guangxi Key R&D Program Project (Grant No. Gui Ke AB21076001) The authors would like to thank the anonymous reviewers for their helpful comments and suggestions.Peer reviewedPostprin

Detection and localization of citrus fruit based on improved You Only Look Once v5s and binocular vision in the orchard

Intelligent detection and localization of mature citrus fruits is a critical challenge in developing an automatic harvesting robot. Variable illumination conditions and different occlusion states are some of the essential issues that must be addressed for the accurate detection and localization of citrus in the orchard environment. In this paper, a novel method for the detection and localization of mature citrus using improved You Only Look Once (YOLO) v5s with binocular vision is proposed. First, a new loss function (polarity binary cross-entropy with logit loss) for YOLO v5s is designed to calculate the loss value of class probability and objectness score, so that a large penalty for false and missing detection is applied during the training process. Second, to recover the missing depth information caused by randomly overlapping background participants, Cr-Cb chromatic mapping, the Otsu thresholding algorithm, and morphological processing are successively used to extract the complete shape of the citrus, and the kriging method is applied to obtain the best linear unbiased estimator for the missing depth value. Finally, the citrus spatial position and posture information are obtained according to the camera imaging model and the geometric features of the citrus. The experimental results show that the recall rates of citrus detection under non-uniform illumination conditions, weak illumination, and well illumination are 99.55%, 98.47%, and 98.48%, respectively, approximately 2–9% higher than those of the original YOLO v5s network. The average error of the distance between the citrus fruit and the camera is 3.98 mm, and the average errors of the citrus diameters in the 3D direction are less than 2.75 mm. The average detection time per frame is 78.96 ms. The results indicate that our method can detect and localize citrus fruits in the complex environment of orchards with high accuracy and speed. Our dataset and codes are available at https://github.com/AshesBen/citrus-detection-localization

Towards automation in the fish processing industry using machine learning

This master project was inspired by challenges faced by commercial fisheries in the north of Norway of controlling food quality and food safety. In this thesis, four different ML models’ ability to do object and keypoint detection on specific anatomy parts of fish, has been studied. With the aim of recommending a suitable model to be part of a CV system for an industrial fish gutting machine that cuts open the fish belly between the pelvic fins and the anus. Requirement that the rotating knife shall not cut into the flesh behind the anus opening, and cut should end (or start) maximum 5 millimeters from the anus opening. Likewise, at the pelvic fins, the cut shall start (or end) 15 millimeters from target along the centerline of the fish, and a sideways offset of roughly ±5 millimeters can be acceptable, depending on the length of the fish. The experiments were performed with two YOLOv7 and two Detectron2 models, YOLOv7 for object detection with bounding boxes, and Detectron2 for keypoint detections. The results showed that only one of the Detectron2 models was able to do keypoint detection repeatedly, but the achieved accuracy was not good enough. Both the YOLOv7 models were able to meet the cut length requirements and both got recommended for use in the suggested CV solution. More work still remains before one of the YOLOv7 models can be taken in use, such as determining the object detection speed, finding a suitable embedded computer with GPU to run the CV system on, determining the best way of communication between the PLC in Folla and the CV system and finding a suitable location for a camera inside the Folla machine

Algoritmos para identificar plantas de maíz para automatizar la fertilización en México

La presente investigación se enfoca en buscar y proponer una solución al problema del agricultor mexicano, dicho problema consiste en cómo se realiza la fertilización en sembradíos de maíz en México. La fertilización manual es un proceso lento, tedioso y poco efectivo; las desventajas pueden ser el mal manejo del fertilizante, aplicación errónea y desperdicio; derivado de esto, se puede decir que la fertilización es un proceso costoso para el agricultor. Se busca responder a la hipótesis sobre si Los algoritmos de inteligencia artificial aplicados en la agricultura mejorarán los procesos al distinguir entre plantas de maíz y maleza, apoyando a la toma de decisiones de los agricultores. Siendo el objetivo principal de la investigación: Identificar plantas de maíz para diferenciarlas de la maleza usando Visión Artificial a partir de un Dataset de imágenes, evaluando el método propuesto a través de un estudio cuantitativo. La metodología implementada incluye un Dataset propio, se emplearon técnicas de clasificación tradicionales (clásicas), los cuales son: Naibe Bayes, Random Forest, K-nn, SVM y Backpropagation, arrojando un porcentaje máximo del 98.98% de precisión realizando pre-procesamiento y segmentación de imágenes con Otsu y el método PCA. También se emplearon las redes neuronales convolucionales de forma individual y en combinación con las SVM a través de un algoritmo híbrido, estas arrojaron un porcentaje máximo de 99.72% de Exactitud y 99.88% de Precisión. Los resultados generados han arrojado que el modelo propuesto en cada una de las etapas de experimentación da respuesta positiva y comprueba que la hipótesis que se planteó es Verdadera. Se plantean nuevos retos derivados de los experimentos y los resultados generados, tales como: aumentar la cantidad de imágenes, realizar más tareas de experimentación con nuevos algoritmos como Adaboost, Gboost y XGBoost entre otros; y facilitar el Dataset en algún repositorio para futuras investigaciones

Plant Physiology, Development and Metabolism

Water is one of the most important constituents of life. Chemically, water is the hydride of oxygen. Oxygen, being more electronegative, exerts a strong attractive pull on its electrons. This unequal attraction results in small positive charge on twohydrogenmoleculesandasmallnegativechargeontheoxygenmolecule.The two lone pairs of electrons of the oxygen molecule result in bending of water molecule. The partial charges on oxygen and hydrogen molecules result in high electric dipole moment and polarity of water molecule

Book of abstracts, 4th World Congress on Agroforestry

International audienc

African Handbook of Climate Change Adaptation

This open access book discusses current thinking and presents the main issues and challenges associated with climate change in Africa. It introduces evidences from studies and projects which show how climate change adaptation is being - and may continue to be successfully implemented in African countries. Thanks to its scope and wide range of themes surrounding climate change, the ambition is that this book will be a lead publication on the topic, which may be regularly updated and hence capture further works. Climate change is a major global challenge. However, some geographical regions are more severly affected than others. One of these regions is the African continent. Due to a combination of unfavourable socio-economic and meteorological conditions, African countries are particularly vulnerable to climate change and its impacts. The recently released IPCC special report "Global Warming of 1.5º C" outlines the fact that keeping global warming by the level of 1.5º C is possible, but also suggested that an increase by 2º C could lead to crises with crops (agriculture fed by rain could drop by 50% in some African countries by 2020) and livestock production, could damage water supplies and pose an additonal threat to coastal areas. The 5th Assessment Report produced by IPCC predicts that wheat may disappear from Africa by 2080, and that maize— a staple—will fall significantly in southern Africa. Also, arid and semi-arid lands are likely to increase by up to 8%, with severe ramifications for livelihoods, poverty eradication and meeting the SDGs. Pursuing appropriate adaptation strategies is thus vital, in order to address the current and future challenges posed by a changing climate. It is against this background that the "African Handbook of Climate Change Adaptation" is being published. It contains papers prepared by scholars, representatives from social movements, practitioners and members of governmental agencies, undertaking research and/or executing climate change projects in Africa, and working with communities across the African continent. Encompassing over 100 contribtions from across Africa, it is the most comprehensive publication on climate change adaptation in Africa ever produced

Modeling the contribution of ecological agriculture for climate change mitigation in cote d'Ivoire

The use of crop models is motivated by the prediction of crop production under climate change and for the evaluation of climate risk adaptation strategies. Therefore, in the present study the performance of DSSAT 4.6 was evaluated in a cropping system involving integrated soil fertility management options that are being promoted as ways of adapting agricultural systems to improve both crop yield and carbon sequestration on highly degraded soils encountered throughout middle Côte d’Ivoire. Experimental data encompassed two seasons in the Guinea savanna zone. Residues from the preceding vegetation were left to dry on plots like mulch on an experimental design that comprised the following treatments: (i) herbaceous savanna-maize, (ii)10 year-old of the shrub Chromolaena odorata fallow-maize (iii) 1 or 2 year-old Lalab pupureus stand-rotation, (iv) the legume L. pupureus -maize rotation; (v) continuous maize crop fertilized with urea; (vi) continuous maize crop fertilized with triple superphosphate; (vii) continuous maize crop, fertilized with both urea and triple superphosphate (TSP); (viii) continuous maize cultivation. The model’s sensitivity analysis was run to figure out how uncertainty of stable organic carbon (SOM3) can generate variation in the prediction of soil organic carbon (SOC) dynamics during the monitoring period of two years, within the first soil layer and to estimate the most suitable value. The observed variations were of 0.05 % in total SOC within the short-term and acceptable dynamics of changes were obtained for 0.80% of SOM3. The DSSAT model was calibrated using data from the 2007-2008 season and validated against independent data sets of yield of 2008-2009 to 2011-2012 cropping seasons. After the default values for SOM3 used in the model was substituted by the estimated one from sensitivity analysis, the model predicted average maize yields of 1 454 kg ha-1 across the sites versus an observed average value of 1 736 kg ha-1, R2 of 0.72 and RMSE of 597 kg ha-1. The impact of fallow residues and cropping sequence on maize yield was simulated and compared to conventional fertilizer and control data using historical climate scenarios over 12 years. Improving soil fertility through conservation agriculture cannot maintain grain yield in the same way as conventional urea inputs, although there is better yield stability against high climate variability according to our results