Peaches Detection Using a Deep Learning Technique - A Contribution to Yield Estimation, Resources Management, and Circular Economy

Fruit detection is crucial for yield estimation and fruit picking system performance. Many state-of-the-art methods for fruit detection use convolutional neural networks (CNNs). This paper presents the results for peach detection by applying a faster R-CNN framework in images captured from an outdoor orchard. Although this method has been used in other studies to detect fruits, there is no research on peaches. Since the fruit colors, sizes, shapes, tree branches, fruit bunches, and distributions in trees are particular, the development of a fruit detection procedure is specific. The results show great potential in using this method to detect this type of fruit. A detection accuracy of 0.90 using the metric average precision (AP) was achieved for fruit detection. Precision agriculture applications, such as deep neural networks (DNNs), as proposed in this paper, can help to mitigate climate change, due to horticultural activities by accurate product prediction, leading to improved resource management (e.g., irrigation water, nutrients, herbicides, pesticides), and helping to reduce food loss and waste via improved agricultural activity scheduling.The authors are thankful to Fundação para a Ciência e Tecnologia (FCT) and R&D Unit “Center for Mechanical and Aerospace Science and Technologies” (C-MAST), under project UIDB/00151/2020, for the opportunity and the financial support to carry on this project. The contributions of Hugo Proença and Pedro Inácio in this work were supported by FCT/MEC through FEDER—PT2020 Partnership Agreement under Project UIDB//50008/2021.info:eu-repo/semantics/publishedVersio

Application of Machine Vision Techniques in Low-Cost Devices to Improve Efficiency in Precision Farming

[EN] In the context of recent technological advancements driven by distributed work and open-source resources, computer vision stands out as an innovative force, transforming how machines interact with and comprehend the visual world around us. This work conceives, designs, implements, and operates a computer vision and artificial intelligence method for object detection with integrated depth estimation. With applications ranging from autonomous fruit-harvesting systems to phenotyping tasks, the proposed Depth Object Detector (DOD) is trained and evaluated using the Microsoft Common Objects in Context dataset and the MinneApple dataset for object and fruit detection, respectively. The DOD is benchmarked against current state-of-the-art models. The results demonstrate the proposed method's efficiency for operation on embedded systems, with a favorable balance between accuracy and speed, making it well suited for real-time applications on edge devices in the context of the Internet of things.This work was partially supported with grant PID2021-123673OB-C31, TED2021-131295BC32 funded by MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe , PROMETEO grant CIPROM/2021/077 from the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital Generalitat Valenciana and Early Research Project grant PAID-06-23 by the Vice Rectorate Office for Research from Universitat Politècnica de València (UPV).Jaramillo-Hernández, JF.; Julian, V.; Marco-Detchart, C.; Rincón, JA. (2024). Application of Machine Vision Techniques in Low-Cost Devices to Improve Efficiency in Precision Farming. Sensors. 24(3). https://doi.org/10.3390/s2403093724

The Use of Agricultural Robots in Orchard Management

Book chapter that summarizes recent research on agricultural robotics in orchard management, including Robotic pruning, Robotic thinning, Robotic spraying, Robotic harvesting, Robotic fruit transportation, and future trends.Comment: 22 page

Counting with convolutional neural networks

2021 Summer.Includes bibliographical references.In this work, we tackle the question: Can neural networks count? More precisely, given an input image with a certain number of objects, can a neural network tell how many are there? To study this, we create a synthetic dataset consisting of black and white images with variable numbers of white triangles on a black background, oriented right-side up, down, left or right. We train a network to count the right-side up triangles; specifically, we see this as a closed-set classification problem where the class is the number of right-side up triangles in the image. These evaluations show that our networks, even in their simplest designs, are able to count a particular object in an image with a very small epsilon of approximation. We conclude that the neural networks are enforced with more complex learning capabilities than given credit for

Sustainable Fruit Growing

Fruit production has faced many challenges in recent years as society seeks to increase fruit consumption while increasing safety and reducing the harmful effects of intensive farming practices (e.g., pesticides and fertilizers). In the last 50 years, the population has more than doubled and is expected to grow to 9 billion people by 2050. Per capita consumption of fruit is also increasing during this time and the global fruit industry is facing a major challenge to produce enough fruit in quantity and quality. The need for sustainable production of nutritious food is critical for human and environmental health.This book provides some answers to people who are increasingly concerned about the sustainability of fruit production and the fruit industry as a whole