Best Quality Tomato Selection by Supply Chain Strategy for Renewable Energy

World agriculture still faces challenges quite fundamental, it is about the quality issues and the increasing of competitiveness through productivity and the eficiency. This research focused on the criteria of best tomato types and how to apply the Simple Additive weighting method (SAW) into a Decision Support System (DSS) for election best quality tomato that can assist farmers in determining the best type of tomato for renewable energy and supply chain strategy, based on the criteria that have been chosen, such as: tomato size, tomato color, tomato shape and tomato disease. By using the application of Simple Additive weighting method into Decision Support Systems value, it can be concluded that V5 is the best quality tomato and has a predicate value of 83.75 with the fragile values, as follows: 50-69 = Enough, 70-82 = Good, 83-100 = Best

Otomatisasi klasifikasi kematangan buah mengkudu berdasarkan warna dan tekstur

 Buah Mengkudu merupakan komoditi ekspor yang sedang berkembang di Indonesia. Proses pengklasifikasian kematangan buah Mengkudu perlu dilakukan agar kualitas buah Mengkudu yang di ekspor dapat terjamin. Proses klasifikasi dengan jumlah yang banyak akan sulit apabila dilakukan secara manual. Oleh karena itu, penelitian ini diperlukan untuk menghasilkan proses otomatisasi klasifikasi kematangan buah Mengkudu. Metode yang diusulkan untuk melakukan otomatisasi klasifikasi adalah proses pengenalan karakteristik buah Mengkudu berdasarkan fitur tekstur dan warna. Fitur tektur dan fitur warna didapatkan melalui proses pengolahan citra digital buah Mengkudu. Penelitian ini membuktikan bahwa pengklasifikasian buah Mengkudu dengan algoritma Support Vector Machines (SVM) menghasilkan nilai persentase lebih tinggi dari pada menggunakan algoritma k-Nearest Neighbors (KNN). Hasil persentase tertinggi yang didapatkan yaitu sebesar 87.22%.   Noni fruit is an export commodities that were flourishing in Indonesia. Noni fruit maturity classification process should be done in order the quality of the noni fruit which is exported can be guaranteed. Classification process in large quantities will be difficult if it is done manually. Therefore this research is needed to produce an automation classification process of noni fruit ripeness. The proposed method is characteristic introduction of noni fruit based on texture and color features. Texture and color features are obtained from digital image processing of noni fruit. This research proves that the classification of noni fruit with SVM algorithm produces better accuracy than using KNN algorithm. The highest accuracy is equal to 87.22%

Comparación de tres sistemas expertos y diferentes espacios de color en la clasificación del grado de madurez de frutos de aguaymanto (PHYSALIS PERUVIANA L.)

RESUMEN La clasificación de frutas frescas según su madurez es un trabajo comúnmente subjetivo y tedioso; en consecuencia, existe un creciente interés en el uso de técnicas no invasivas como las basadas en la visión computarizada y en técnicas de aprendizaje automatizado. En esta investigación, proponemos el uso de técnicas no invasivas para la clasificación de frutos de aguaymanto. La propuesta se basa en el uso de técnicas de aprendizaje automático combinadas con diferentes espacios de color. Dado el éxito que han tenido las técnicas automatizadas como las redes neuronales artificiales, máquinas de soporte vectorial, árboles de decisiones y el método de los K vecinos más próximos, en los problemas de clasificación, decidimos utilizar estos alcances en el presente trabajo de investigación. Se obtuvo una muestra de 819 frutos de aguaymanto, los cuales fueron clasificados manualmente según su nivel de madurez en siete clases diferentes. Las imágenes de cada fruta fueron obtenidas en formato RGB a través de un sistema desarrollado para este fin. Estas imágenes fueron pre-procesadas, filtradas y segmentadas hasta la identificación de los frutos. Para cada una de las frutas, se obtuvieron los valores medianos de sus parámetros de color en el espacio RGB, y subsecuentemente se transformaron los resultados en los espacios de color HSV y L*a*b*. Los valores de cada fruto en los tres espacios de color y sus correspondientes grados de madurez fueron utilizados para la creación, validación y comparación de los modelos de clasificación desarrollados. Se halló que la elección de uno u otro espacio de color, afecta la calidad del clasificador. Los sistemas basados en árboles de decisiones ofrecen los mejores resultados, estos fueron mayores a 97% de precisión con 18 y con 6 parámetros de interés y mayores a 72% al combinarlos con los espacios de color RGB, HSV y L*a*b*. Los modelos basados en el método de las redes neuronales artificiales obtienen resultados más variables. Los modelos basados en el espacio de color L*a*b* ofrecen los mejores resultados, estos fueron superiores a 72% de precisión. Finalmente, el modelo que mejor clasifica los frutos de aguaymanto de acuerdo con su nivel de madurez es el que resultó de la combinación de la técnica SVM y el espacio de color RGB, obteniendo una medida F de 79,47% y una precisión de 79,79%. PALABRAS CLAVES: Aguaymanto, espacios de color, redes neuronales artificiales, máquinas de soporte vectorial, árboles de decisiones, K-vecinos más próximos.ABSTRACT The classification of fresh fruits according to their ripeness is commonly a subjective and tedious task; consequently, there is growing interest in the use of non-contact techniques as such those based on computer vision and machine learning. In this paper, we propose the use of non-intrusive techniques for the classification of Cape gooseberry fruits. The proposal is based on the use of machine learning techniques combined with different color spaces. Given the success of techniques such as artificial neural networks, support vector machines, decision trees, and Knearest neighbors in classification problems, we decided to use these approaches in this research work. A sample of 819 Cape gooseberry fruits was obtained, and fruits were classified manually according to their level of ripeness in seven different classes. Images of each fruit were acquired in the RGB format through a system developed for this purpose. These images were preprocessed, filtered and segmented until the fruits were identified. For each piece of fruit, the median color parameter values in the RGB space were obtained, and these results were subsequently transformed into the HSV and L*a*b* color spaces. The values of each piece of fruit in the three color spaces and their corresponding degrees of ripeness were arranged for use in the creation, validation, and comparison of the developed classification models. The choice of color space was found to affect the quality of the classifier. Decision trees based systems offer the best results, the precision of these where higher than 97% when using 18 parameters and 6 parameters of interest, and higher than 72% when combined with RGB, HSV and L*a*b* color spaces. The artificial neural network-based models obtain more variable results. The models based on the L*a*b* color space offer the best results, the precision of these where superior than 72%. Finally, the model that best classifies the cape gooseberry fruits based on ripeness level is that resulting from the combination of the SVM technique and the RGB color space, obtaining an F measure of 79,47% and accuracy of 79,79%. KEYWORDS: Golden Berry, color spaces, artificial neural networks, support vector machines, decision trees, K-nearest neighbors

A CNN-ELM Classification Model for Automated Tomato Maturity Grading

Tomatoes are popular around the world due to their high nutritional value. Tomatoes are also one of the world’s most widely cultivated and profitable crops. The distribution and marketing of tomatoes depend highly on their quality. Estimating tomato ripeness is an essential step in determining shelf life and quality. With the abundant supply of tomatoes on the market, it is exceedingly difficult to estimate tomato ripeness using human graders. To address this issue and improve tomato quality inspection and sorting, automated tomato maturity classification models based on different features have been developed. However, current methods heavily rely on human-engineered or handcrafted features. Convolutional neural networks have emerged as the preferred technique for general object recognition problems because they can automatically detect and extract valuable features by directly working on input images. This paper proposes a CNN-ELM classification model for automated tomato maturity grading that combines CNNs’ automated feature learning capabilities with the efficiency of extreme learning machines to perform fast and accurate classification even with limited training data. The results showed that the proposed CNN-ELM model had a classification accuracy of 96.67% and an F1-score of 96.67% in identifying six maturity stages from the test data

Desarrollo de algoritmo y prototipo móvil para medir el grado de madurez del aguacate Hass mediante procesamiento digital de imágenes

La producción de aguacate Hass está presente en diferentes regiones de Colombia. Los agricultores cosechan el aguacate cuando ha alcanzado su madurez fisiológica y desde allí puede ser dispendioso conocer su estado de madurez para los comercializadores o consumidores. Por ello se hace necesario tener conocimiento sobre el estado de maduración del fruto con ayuda de herramientas tecnológicas para facilitar su clasificación en base a su madurez y determinar su tiempo de vida, proporcionando detalles precisos para su exportación y venta regional. Los principales criterios de maduración son el cambio de color y pérdida de brillo de la fruta, los cuales pueden ser poco precisos debido a la subjetividad de cada persona. La idea principal es capturar el color y brillo del aguacate por medio de imágenes digitales para analizar su estado y obtener su clasificación

Strawberry Ripeness Assessment Via Camouflage-Based Data Augmentation for Automated Strawberry Picking Robot

Vision-based strawberry picking and placing is one of the main objectives for strawberry harvesting robots to complete visual servoing procedures accurately. Occlusion is the main challenge in strawberry ripeness detection for agriculture robots. In this study, strawberry ripeness detection was proposed using a camouflage-based data augmentation strategy to simulate the natural environment of strawberry harvesting conditions. Yolov4, Yolov4 tiny and Yolov4 scaled, and their traditional data augmentation and camouflage-based data augmentation derivatives were used to find out the effect of camouflage-based augmentation technique in overcoming the occlusion issue. Then the results were mainly evaluated based on mean Intersection over Union (IoU), F-1 score, average precision (AP) for ripe and unripe strawberries and frame per second (fps). Yolov4 tiny with camouflage-based data augmentation technique has demonstrated superior performance in detecting ripe and unripe strawberries with 84% IoU accuracy ~99% AP for ripe and unripe strawberries at an average of 206-fps, satisfying the agriculture strawberry harvesting robot operation need. The performance of the suggested technique was then tested successfully using a dataset termed the challenge dataset in this study to demonstrate its performance in a complex and occluded strawberry harvesting environment. Camouflage-based data augmentation technique helps to increase the detection procedure of ripe and unripe strawberries toward autonomous strawberry harvesting robot

Determinación de la madurez de mazorcas de Cacao, haciendo uso de redes neuronales convolucionales en un sistema embebido

A correct cocoa harvest involves determining a pod maturity. However, this farm activity is usually handmade, using criteria such as Size and Color of the pod; those characteristics differ according to the cocoa variety, making it difficult to standardize. For this reason, this work proposes an automated method to simplify the number of variables to develop a portable, low-cost, and custom-made tool, which makes use of a convolutional neural network to indicate whether a cocoa pod is found it at the right time to harvest. The main results of this work are: 1) the construction of three labeled data sets (1992 images each), and 2) we developed an embedded system with a 34.83% mAP (mean Average Precision) accuracy. Finally, variance analysis demonstrates that image size (i.e., 4033x4033 p, 1009x1009 p, and 505x505 p) does not affect accuracy.Una correcta cosecha Cacao implica determinar si la mazorca se encuentra en un adecuado estado de madurez. No obstante, este proceso suele darse de manera artesanal y basarse en atributos como el tamaño y color de la mazorca, características que difieren según la variedad cultivada, lo cual dificulta su estandarización. Con el fin de simplificar la cantidad de variables y presentar un método automatizado, el presente trabajo propone desarrollar una herramienta portable, de bajo costo, y hecha a medida, la cual hace uso de una red neuronal convolucional para indicar si una mazorca de cacao se encuentra en el momento oportuno para ser cosechada. Entre los principales resultados del presente trabajo se encuentran: 1) la construcción de tres conjuntos de datos etiquetados (1992 imágenes cada uno), y 2) un sistema embebido con una precisión de 34.83% mAP (mean Average Precision). Finalmente, se demuestra estadísticamente que el tamaño de las imágenes (4033x4033 p, 1009x1009 p y 505x505 p) no incide sobre la eficacia del entrenamiento

Convolutional neural network ensemble learning for hyperspectral imaging-based blackberry fruit ripeness detection in uncontrolled farm environment

Fruit ripeness estimation models have for decades depended on spectral index features or colour-based features, such as mean, standard deviation, skewness, colour moments, and/or histograms for learning traits of fruit ripeness. Recently, few studies have explored the use of deep learning techniques to extract features from images of fruits with visible ripeness cues. However, the blackberry (Rubus fruticosus) fruit does not show obvious and reliable visible traits of ripeness when mature and therefore poses great difficulty to fruit pickers. The mature blackberry, to the human eye, is black before, during, and post-ripening. To address this engineering application challenge, this paper proposes a novel multi-input convolutional neural network (CNN) ensemble classifier for detecting subtle traits of ripeness in blackberry fruits. The multi-input CNN was created from a pre-trained visual geometry group 16-layer deep convolutional network (VGG16) model trained on the ImageNet dataset. The fully connected layers were optimized for learning traits of ripeness of mature blackberry fruits. The resulting model served as the base for building homogeneous ensemble learners that were ensemble using the stack generalization ensemble (SGE) framework. The input to the network is images acquired with a stereo sensor using visible and near-infrared (VIS-NIR) spectral filters at wavelengths of 700 nm and 770 nm. Through experiments, the proposed model achieved 95.1% accuracy on unseen sets and 90.2% accuracy with in-field conditions. Further experiments reveal that machine sensory is highly and positively correlated to human sensory over blackberry fruit skin texture