Development of a system based on aerial images for the morphological patterns classification using support vector machine

Abstract

Oil palm cultivation is one of the major agricultural activities in Colombia. Production performance is related to the good practices in the plantation, mainly regarding the management of phytosanitary conditions. Bud rot disease is the one with the greatest impact in Colombia. The most commonly used technique for its detection is from routine visual inspection on each palm, being costly and inefficient. For this reason, the aim of this study is the development of a classification algorithm based on binary support vector machines for the detection of Bud Rot. The model was obtained from 798 aerial images acquired by unmanned aerial vehicles. Each image was tagged by an expert palm grower based on the presence or absence of the disease. These images were described by 531 morphological features extracted using the concatenation of uniform binary local pattern vectors. Bootstrapping was used to balance the classes, obtaining 507 observations per class. To evaluate the performance metrics of the classifier, an 8-fold Monte Carlo cross-validation was implemented by randomly splitting the data set into training (80%), validation (10%), and test (10%) sets with balanced classes. Finally, the model achieved a performance greater than 96.0%. This indicates that the model developed could be a great technique to automate bud rot detection with high reliability, increasing the efficiency in the recognition. All these thanks to the fusion of Machine Learning techniques with the phenomena of optical physics.

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

“…Oil palm diseases have been studied using a wide variety of diagnostic methods. Categories such as SVM [120], as well as ANN [135] and NB [152], are included. In this regard, traditional ML methods have some drawbacks when it comes to monitoring palm oil disease.…”

A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction

“…MAPE is used to calculate the average of the percentage errors that determines how far the prediction of the model deviates from its corresponding outcomes [53]. The machine learning based classification algorithms for crop yield prediction are evaluated by accuracy [87] [88], precision [89], recall [89], sensitivity [90], specificity [90], and F1 Score [91]. However, classification accuracy is the most widely used and effective metric for classification problems.…”

“…[140] implemented an identification method for palm oil yield with 85% detection performance. Montero et al [90] proposed a classification algorithm with binary SVM using 798 aerial images from their UAV for the detection of Bud Rot. Although the system's accuracy is high, this study's dataset was insufficient.…”

“…Researchers have studied a wide range of diagnostic methods for oil palm diseases. These categories include: SVM [90], CNN [88], ANN [58], NB [140], and MLR [141]. Thus, when applying traditional ML methods to monitor palm oil diseases, there are some disadvantages.…”

Discovering the Ganoderma Boninense Detection Methods Using Machine Learning: A Review of Manual, Laboratory, and Remote Approaches

“…Montero et al [49] developed a classification model based on binary SVM to detect bud rot (BR). Bootstrapping was applied to balance the classes.…”