Oil palm female inflorescences anthesis stages identification using selected emissivities through thermal imaging and Machine Learning - Yousefidashliboroun, Mamehgol (2022)
Oil palm industry seeks for a reduction of cost and environmental impact, promote sustainability and to maximize crop production and quality. In the oil palm production process, pollination is one of the main factors contributing to yield. However, oil palm pollination is facing issues with fruit formation and filling due to poor natural pollination. Alternatively, assisted/artificial pollination and Wireless Sensor Network (WSN) systems have been introduced to determine pollination readiness of the oil palms, break the reproduction cycle, and producing new breeding material. To perform these methods, several factors are taken into account such as the number of inflorescences to be pollinated per hectare and if these inflorescences require the opening of bracts. These tasks are labor-intensive and subjective to the availability of experts. All these methods depend on manual monitoring and visual inspection with ever decreasing labor, making farming economically inefficient and unstable. Therefore, it’s necessary to identify the pollination stages to ensure successful assisted/artificial pollination operation. To achieve this in digital agriculture, useful data about the different stages of oil palms inflorescences pollination is necessary to deliver better decision-making systems. This research studies different Machine Learning (ML) classification and ensemble techniques for the assessment of the four pollination stages consist of pre-anthesis I, pre-anthesis II, pre-anthesis III, and anthesis using thermal imaging. Different ML algorithms such as Random Forest (RF), k Nearest Neighbor (kNN), Support Vector Machine (SVM), Artificial Neural Network (ANN) as well as an ensemble method are used on data extracted from thermal images collected during infield oil palms pollination stages monitoring. Thermal images are captured with a selected emissivity values of 0.96, 0.97, and 0.98 and evaluated to determine the best model performance. To apply the above-mentioned models, there are two feature sets are utilized consisting of endogenous features from thermal images taken with three emissivity values and exogenous features including meteorological variables. These models’ performance is validated statistically and empirically considering the average accuracy, recall, precision, and F-measure in classifying the present datasets. The ensemble method on endogenous and endogenous+exogenous feature sets from emissivity of 0.96 achieved F-measure scores of 92.68% and 93.42% respectively and with emissivity of 0.97 resulted in 87.06% and 89.73% respectively. However, the ensemble method on emissivity of 0.98 using endogenous and endogenous+exogenous feature sets resulted in F-measure score of 57.81% and 86.63%, respectively lower than that of the latter. Ultimately, the results suggest that the proposed ML method can be utilized effectively to accurately estimate the four pollination stages in plantations, becoming a reliable and accurate tool in automated assisted/artificial pollination decision making systems. The proposed detection system capable of rapid and accurate screening and identification of oil palms inflorescences can be applied.