Quantifying the Production of Fruit-Bearing Trees Using Image Processing Techniques

[EN] In recent years, the growth rate of world agricultural production and crop yields have decreased. Crop irrigation becomes essential in very dry areas and where rainfall is scarce, as in Egypt. Persimmon needs low humidity to obtain an optimal crop. This article proposes the monitoring of its performance, in order to regulate the amount of water needed for each tree at any time. In our work we present a technique that consists of obtaining images of some of the trees with fruit, which are subsequently treated, to obtain reliable harvest data. This technique allows us to have control and predictions of the harvest. Also, we present the results obtained in a first trial, through which we demonstrate the feasibility of using the system to meet the objectives set. We use 5 different trees in our experiment. Their fruit production is different (between 20 and 47kg of fruit). The correlation coefficient of the obtained regression model is 0.97.This work has been partially supported by European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR by the Conselleria de Educación, Cultura y Deporte with the Subvenciones para la contratación de personal investigador en fase postdoctoral, grant number APOSTD/2019/04, and by the Cooperativa Agrícola Sant Bernat Coop.V.García, L.; Parra-Boronat, L.; Basterrechea-Chertudi, DA.; Jimenez, JM.; Rocher-Morant, J.; Parra-Boronat, M.; García-Navas, JL.... (2019). Quantifying the Production of Fruit-Bearing Trees Using Image Processing Techniques. IARIA XPS Press. 14-19. http://hdl.handle.net/10251/180619S141

Design and Performance of Solar-Powered Surveillance Robot for Agriculture Application

Agriculture can benefit from robotics technology to overcome the drawback of limited human labor working in this sector. One of the robot applications in agriculture is a surveillance robot to monitor the condition. This paper describes a surveillance robot that is powered by a capacitor bank charged by a mini solar panel. The solar-powered robot is well-suited for deployment in open agricultural areas in Indonesia, where the irradiance is high. This potential is excellent for generating electricity and charging electric vehicles, such as those used in agriculture. The surveillance robot developed and tested in this study has been successfully deployed in an agriculture-like setting with all-terrain contours and the capacity to avoid obstacles. During high irradiance sunny weather, the shortest charging time was 2 hours. Hence, the proposed technology is effective for designing a surveillance robot for agricultural applications

Preliminary results of peach detection in images applying convolutional neuronal network

The fruit detection part is very important for a good performance in a yield estimation system. This paper presents the preliminary results using the object detection Faster R-CNN method in the peaches images. The aim is evaluate the method performance in the detection of peach RGB images. Images acquired in an orchard were used. Although this method of object detection has been applied in other studies to detect fruits, according to the literature, it has not been used to detect peaches. The results, although preliminary, show a great potential of using the method to detect peach.Este trabalho de investigação é financiado pelo projeto PrunusBot - Sistema robótico aéreo autónomo de pulverização controlada e previsão de produção frutícola, Operação n.º PDR2020-101-031358 (líder), Consórcio n.º 340, Iniciativa n.º 140, promovido pelo PDR2020 e co-financiado pelo FEADER e União Europeia no âmbito do Programa Portugal 2020.info:eu-repo/semantics/publishedVersio

Rancang Bangun Sistem Penentuan Mutu Buah Lemon Berbasis Pengolahan Citra

Lemon merupakan buah yang memiliki banyak manfaat dan dapat dinilai kualitasnya berdasarkan kematangan, ukuran, dan cacat. Warna dan ukuran adalah parameter utama bagi konsumen dalam menentukan kualitas lemon. Saat ini, mayoritas proses penentuan mutu lemon di Indonesia masih dilakukan secara manual. Penentuan mutu lemon secara manual merupakan pekerjaan yang melelahkan dan memakan banyak waktu dan biaya. Sebuah sistem penentuan mutu lemon secara otomatis dapat mengurangi biaya yang dikeluarkan oleh petani untuk tenaga kerja. Pada penelitian ini akan dibuat sebuah sistem yang dapat menentukan tingkatan mutu lemon dengan menganalisa warna dan ukuran lemon. Sistem yang dirancang terdiri dari komputer Raspberry Pi 2B, kamera, dan sistem pencahayaan untuk pengambilan danpemrosesan citra lemon. Tiga jenis klasifikasi dilakukan oleh sistem, yaitu klasifikasi berdasarkan ukuran, warna, dan ada tidaknya cacat. Dengan metode tersebut, sistem yang dibuat memiliki performa yang cukup baik yaitu sebesar 47,08%. Sistem yang dibuat dapat mengukur diameter lemon dengan akurasi sebesar 98,79%, dan dapat menentukan mutu dalam waktu 0,343 detik

Neural Network-Based Image Processing for Tomato Harvesting Robot

Agriculture is one of the areas that can benefit from robotics technology, as it faces issues such as a shortage of human labor and access to less arid terrain. Harvesting is an important step in agriculture since workers are required to work around the clock. The red ripe tomatoes should go to the nearest market, while the greenest should go to the farthest market. Harvesting robots can benefit from Neural Network-based image processing to ensure robust detection. The vision system should assist the mobility system in moving precisely and at the appropriate speed. The design and implementation of a harvesting robot are described in this study. The efficiency of the proposed strategy is tested by picking red-ripened tomatoes while leaving the yellowish ones out of the experimental test bed. The experiment results demonstrate that the effectiveness of the proposed method in harvesting the right tomatoes is 80%

MOTION CONTROL ANALYSIS OF TWO COLLABORATIVE ARM ROBOTS IN FRUIT PACKAGING SYSTEM

As robots' use increases in every sector of human life, the demand for cheap and efficient robots has also enlarged. The use of two or more simple robot is preferable to the use of one sophisticated robot. The agriculture industry can benefit from installing a robot, from seeding to the packaging of the product. A precise analysis is required for the installation of two collaborative robots. This paper discusses the motion control analysis of two collaborative arms robots in the fruit packaging system. The study begins with the relative motion analysis between two robots, starting with kinematics modeling, image processing for object detection, and the Fuzzy Logic Controller's design to show the relationship between the robot inputs and outputs. The analysis is carried out using SCILAB, open-source software for numerical computing engineering. This paper is intended as the initial analysis of the feasibility of the real experimental system

Apple (Malus domestica) and pear (Pyrus communis) yield prediction after tree image analysis

Yield forecasting depends on accurate tree fruit counts and mean size estimation. This information is generally obtained manually, requiring many hours of work. Artificial vision emerges as an interesting alternative to obtaining more information in less time. This study aimed to test and train YOLO pre-trained models based on neural networks for the detection and count of pears and apples on trees after image analysis; while also estimating fruit size. Images of trees were taken during the day and at night in apple and pear trees while fruits were manually counted. Trained models were evaluated according to recall, precision and F1score. The correlation between detected and counted fruits was calculated while fruit size estimation was made after drawing straight lines on each fruit and using reference elements. The precision, recall and F1score achieved by the models were up to 0.86, 0.83 and 0.84, respectively. Correlation coefficients between fruit sizes measured manually and by images were 0.73 for apples and 0.80 for pears. The proposed methodologies showed promising results, allowing forecasters to make less time consuming and accurate estimates compared to manual measurements. Highlights The number of fruits in apple and pear trees, could be estimated from images with promising results. The possibility of estimating the fruit numbers from images could reduce the time spent on this task, and above all, the costs. This allow growers to increase the number of trees sampled to make yield forecasts.Yield forecasting depends on accurate tree fruit counts and mean size estimation. This information is generally obtained manually, requiring many hours of work. Artificial vision emerges as an interesting alternative to obtaining more information in less time. This study aimed to test and train YOLO pre-trained models based on neural networks for the detection and count of pears and apples on trees after image analysis; while also estimating fruit size. Images of trees were taken during the day and at night in apple and pear trees while fruits were manually counted. Trained models were evaluated according to recall, precision and F1score. The correlation between detected and counted fruits was calculated while fruit size estimation was made after drawing straight lines on each fruit and using reference elements. The precision, recall and F1score achieved by the models were up to 0.86, 0.83 and 0.84, respectively. Correlation coefficients between fruit sizes measured manually and by images were 0.73 for apples and 0.80 for pears. The proposed methodologies showed promising results, allowing forecasters to make less time consuming and accurate estimates compared to manual measurements. Highlights The number of fruits in apple and pear trees, could be estimated from images with promising results. The possibility of estimating the fruit numbers from images could reduce the time spent on this task, and above all, the costs. This allow growers to increase the number of trees sampled to make yield forecasts

Combining computer vision and deep learning to classify varieties of Prunus dulcis for the nursery plant industry

Varietal control to avoid unwanted varietal mixtures is an important objective for the nursery plant industry. In this study, we have developed and analyzed the capabilities of a computer vision system based on deep learning for the control of plant varieties in the nursery plant industry and for evaluating its capabilities. For this purpose, three datasets of nursery plant images were compared. The datasets came from two varieties of almond trees (Prunus dulcis) named Soleta and Pentacebas. Each dataset contained images with three different scales: whole plant, leaf, and venation. The Gradient-weighted Class Activation Mapping (Grad-CAM) technique was used to unveil the most important features to discriminate between both varieties. The three datasets provided classification accuracies above 97% in the test set, being the leaf dataset, with a 98.8% accuracy, the one providing the best results. Concerning the most important features of the plants, the Grad-CAM showed that they are located in the center of the leaf, that is, the venation. In conclusion, we have shown that computer vision is a promising technique for the control of plant varietal mixtures.Spanish Ministry of Economy andCompetitiveness, Grant/Award Number:AGL2015-70106-R, AEI/FEDER, UE;Industrial Doctorates Plan of theSecretariat of Universities and Research ofthe Department of Economy andKnowledge of the Generalitat of Catalonia,Grant/Award Number: DI-COF 2017Peer ReviewedPostprint (published version

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

Apple Flower Detection Using Deep Convolutional Networks

To optimize fruit production, a portion of the flowers and fruitlets of apple trees must be removed early in the growing season. The proportion to be removed is determined by the bloom intensity, i.e., the number of flowers present in the orchard. Several automated computer vision systems have been proposed to estimate bloom intensity, but their overall performance is still far from satisfactory even in relatively controlled environments. With the goal of devising a technique for flower identification which is robust to clutter and to changes in illumination, this paper presents a method in which a pre-trained convolutional neural network is fine-tuned to become specially sensitive to flowers. Experimental results on a challenging dataset demonstrate that our method significantly outperforms three approaches that represent the state of the art in flower detection, with recall and precision rates higher than 90%. Moreover, a performance assessment on three additional datasets previously unseen by the network, which consist of different flower species and were acquired under different conditions, reveals that the proposed method highly surpasses baseline approaches in terms of generalization capability