Thailand is the world's largest exporter of cassava. The cassava prices fluctuate because of many factors such as the production cost; economic condition; and price intervention. Therefore; this research aims to propose a forecasting model of cassava price based on the 11-year data (from 2005 to 2015) obtained from the Thai Tapioca Starch Association and Office of Agricultural Economics. Various techniques were applied for the forecast such as Artificial Neural Network; Support Vector Machine; k-Nearest Neighbor and Hybrid Technique. The statistics used to determine the effectiveness of this model were Mean Absolute Percentage Error (MAPE); Root Mean Squared Error (RMSE) and Mean Squared Error (MSE). The results of this research showed that Hybrid Technique demonstrated the lowest value of error followed by Artificial Neural Network; k-Nearest Neighbor and Support Vector Machine; respectively. Therefore; it could be concluded that using the Hybrid Technique to forecast the price of cassava was better than other techniques and generated the predicted price closest to the actual price.

Plant diseases are one of the most serious issues that can decrease the value and volume of plant goods. It is time-consuming for farmers to discover and identify the disease by observing the leaves of plants; even with specialists scientists and laboratory processes. This study proposed the deep learning approach to address the real-world problems that are contained in the PlantDoc dataset. The deep learning method aims to classify plant leaf disease images from the PlantDoc dataset. First; four state-of-the-art convolutional neural networks (CNNs): VGG16; MobileNetV2; InceptionResNetV2; and DenseNet201; were proposed to enhance the plant leaf disease classification performance. As a result; for the baseline CNN model; DenseNet201 showed better performance with an accuracy of 67.18%; while the second-best CNN model was the InceptionResNetV2 with an accuracy of 61.75%. In addition; the data augmentation techniques (rotation; zoom; brightness; cutout; and mixup) were combined in the training process. The InceptionRes-NetV2 when combined with the rotation technique obtained an accuracy of 66.02% and outperformed all other CNNs. Importantly; based on our experimental results; the data augmentation techniques with brightness; cutout; and mixup were less satisfactory on the PlantDoc dataset. Second; we proposed the snapshot ensemble to improve the performance of the CNN models. We evaluated the classification performance by applying the snapshot ensemble with 4 and 5 cosine annealing cycles and optimized the learning rate using a stochastic gradient descent algorithm. We also examined the snapshot ensemble with the weighted and unweighted ensemble methods. The experimental results showed that the DenseNet201 when training with the snapshot ensemble method (4-cycle) obtained the accuracy of 69.51%.

The use of machine learning and computer vision methods for recognizing different plants from images has attracted lots of attention from the community. This paper aims at comparing local feature descriptors and bags of visual words with different classifiers to deep convolutional neural networks (CNNs) on three plant datasets; AgrilPlant; LeafSnap; and Folio. To achieve this; we study the use of both scratch and fine-tuned versions of the GoogleNet and the AlexNet architectures and compare them to a local feature descriptor with k-nearest neighbors and the bag of visual words with the histogram of oriented gradients combined with either support vector machines and multi-layer perceptrons. The results shows that the deep CNN methods outperform the hand-crafted features. The CNN techniques can also learn well on a relatively small dataset; Folio.

There is a global increase in health awareness. The awareness of changing eating habits and choosing foods wisely are key factors that make for a healthy life. In order to design a food image recognition system; many food images were captured from a mobile device but sometimes include non-food objects such as people; cutlery; and even food decoration styles; called noise food images. These issues decreased the performance of the system. Convolutional neural network (CNN) architectures are proposed to address this issue and obtain good performance. In this study; we proposed to use the ResNet50-LSTM network to improve the efficiency of the food image recognition system. The state-of-the-art ResNet architecture was invented to extract the robust features from food images and was employed as the input data for the Conv1D combined with a long short-term memory (LSTM) network called Conv1D-LSTM. Then; the output of the LSTM was assigned to the global average pooling layer before passing to the softmax function to create a probability distribution. While training the CNN model; mixed data augmentation techniques were applied and increased by 0.6%. The results showed that the ResNet50+Conv1D-LSTM network outperformed the previous works on the Food-101 dataset. The best performance of the ResNet50+Conv1D-LSTM network achieved an accuracy of 90.87%.

This paper presents to compare prediction models for road deaths on road network by data mining techniques. In this work; the classifier is selected from four prediction algorithms: Random Forest (RF); Support Vector Machine (SVM); K-Nearest Neighbor (KNN); and Neural Network (NN). The dead injured and dead people data in road accident data set of the Ministry of Transport; Thailand from January to April 2021. It has up to 8;560 records 46 attributes. This research has measured performance models with accuracy; precision; recall; and f-measure. The comparative results showed that the accuracy of RF is the most appropriate for predicting road deaths on road network with accuracy 89%; precision 0.86; recall 0.89; and f-measure 0.85.

Recognition of plant leaves and diseases from images is a challenging task in computer vision and machine learning. This is because various problems directly affect the performance of the system; such as the leaf structure; differences of the intra-class; similarity of shape between inter-class; perspective of the image; and even recording time. In this paper; we propose the ensemble convolutional neural network (CNN) method to tackle these issues and improve plant leaf recognition performance. We trained five CNN models: MobileNetV1; MobileNetV2; NASNetMobile; DenseNet121; and Xception; accordingly to discover the best CNN based model. Ensemble methods; unweighted average; weighted average; and unweighted majority vote methods were then applied to the CNN output probabilities of each model. We have evaluated these ensemble CNN methods on a mulberry leaf dataset and two leaf disease datasets: tomato and corn leaf disease. As a result; the individual CNN model shows that MobileNetV2 outperforms every CNN model with an accuracy of 91.19% on the mulberry leaf dataset. The Xception combined with data augmentation techniques (Height Shift+Vertical Flip+Fill Mode) obtains an accuracy of 91.77%. We achieved very high accuracy above 99% from the DenseNet121 and Xception models on the leaf disease datasets. For the ensemble CNNs method; we selected the based models according to the best CNN models and predicted the output of each CNN with the weighted average ensemble method. The results showed that 3-Ensemble CNNs (3-EnsCNNs) performed better on plant leaf disease datasets; while 5-EnsCNNs outperforms on the mulberry leaf dataset. Surprisingly; the data augmentation technique did not affect the ensemble CNNs on the mulberry leaf and corn leaf disease datasets. On the other hand; application of data augmentation was slightly better than without only on the tomato leaf disease dataset.

Use of ensemble convolutional neural networks (CNNs) has become a more robust strategy to improve image classification performance. However; the success of the ensemble method depends on appropriately selecting the optimal weighted parameters. This paper aims to automatically optimize the weighted parameters using the differential evolution (DE) algorithm. The DE algorithm is applied to the weighted parameters and then assigning the optimal weighted to the ensemble method and stacked ensemble method. For the ensemble method; the weighted average ensemble method is applied. For the stacked ensemble method; we use the support vector machine for the second-level classifier. In the experiments; firstly; we experimented with discovering the baseline CNN models and found the best models on the pornographic image dataset were NASNetLarge with an accuracy of 93.63%. Additionally; three CNN models; including EfficientNetB1; InceptionResNetV2; and MobileNetV2; also obtained an accuracy above 92%. Secondly; we generated two ensemble CNN frameworks; the ensemble learning method; called Ensemble-CNN and the stacked ensemble learning method; called StackedEnsemble-CNN. In the framework; we optimized the weighted parameter using the DE algorithm with six mutation strategies containing rand/1; rand/2; best/1; best/2; current to best/1; and random to best/1. Therefore; the optimal weighted was given to classify using ensemble and stacked ensemble methods. The result showed that the Ensemble-3CNN and StackedEnsemble-3CNN; when optimized using the best/2 mutation strategy; surpassed other mutation strategies with an accuracy of 96.83%. The results indicated that we could create the learning method framework with only 3 CNN models; including NASNetLarge; EfficientNetB1; and InceptionResNetV2.