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In digital agriculture, accurate crop detection is fundamental to developing automated systems for efficient plantation management. For oil palm, the main challenge lies in developing robust models that perform well in different environmental conditions. This study addresses the feasibility of using GAN augmentation methods to improve palm detection models. For this purpose, drone images of young palms (< 5 year-old) from eight different estates were collected, annotated, and used to build a baseline detection model based on DETR. StyleGAN2 was trained on the extracted palms and then used to generate a series of synthetic palms, which were then inserted into tiles representing different environments. CycleGAN networks were trained for bidirectional translation between synthetic and real tiles, subsequently utilized to augment the authenticity of synthetic tiles. Both synthetic and real tiles were used to train the GAN-based detection model. The baseline model achieved precision and recall values of 95.8% and 97.2%. The GAN-based model achieved comparable result, with precision and recall values of 98.5% and 98.6%. In the challenge dataset 1 consisting older palms (> 5 year-old), both models also achieved similar accuracies, with baseline model achieving precision and recall of 93.1% and 99.4%, and GAN-based model achieving 95.7% and 99.4%. As for the challenge dataset 2 consisting of storm affected palms, the baseline model achieved precision of 100% but recall was only 13%. The GAN-based model achieved a significantly better result, with a precision and recall values of 98.7% and 95.3%. This result demonstrates that images generated by GANs have the potential to enhance the accuracies of palm detection models. [ABSTRACT FROM AUTHOR]

During the milling process of palm oil, the degree of palm fruit ripeness is a critical factor that affects the quality and quantity of the oil. As the palm fruit matures, its chlorophyll level decreases, and since chlorophyll in oil has undesirable effects on hydrogenation, bleachability, and oxidative degradation, it's important to monitor the chlorophyll content in palm oil during the milling process. This study investigated the use of light-induced chlorophyll fluorescence (LICF) for non-invasive and real-time monitoring of chlorophyll content in diluted crude palm oil (DCO) located at the dilution and oil classification point in palm oil mill. An LICF probe was installed at the secondary pipe connected to main DCO pipeline, and the system communicates with a computer located in a separate control room via a Wi-Fi connection. Continuous measurements were recorded with an integration time of 500 ms, averaging of 10, and a time interval of 1 min between each recording during the oil mill's operation. All data were stored on the computer and in the cloud. We collected 60 DCO samples and sent them to the laboratory for American Oil Chemists' Society (AOCS) measurement to compare with the LICF signal. The LICF method achieved a correlation coefficient of 0.88 with the AOCS measurements, and it also provided a direct, quantitative, and unbiased assessment of the fruit ripeness in the mill. By incorporating Internet of Things (IoT) sensors and cloud storage, this LICF system enables remote and real-time access to data for chemometrics analysis. [ABSTRACT FROM AUTHOR]

Stomatal density is an important trait for breeding selection of drought tolerant oil palms; however, its measurement is extremely tedious. To accelerate this process, we developed an automated system. Leaf samples from 128 palms ranging from nursery (1 years old), juvenile (2–3 years old) and mature (> 10 years old) were collected to build an oil palm specific stomata detection model. Micrographs were split into tiles, then used to train a stomata object detection convolutional neural network model through transfer learning. The detection model was then tested on leaf samples acquired from three independent oil palm populations of young seedlings (A), juveniles (B) and productive adults (C). The detection accuracy, measured in precision and recall, was 98.00% and 99.50% for set A, 99.70% and 97.65% for set B, and 99.55% and 99.62% for set C, respectively. The detection model was cross-applied to another set of adult palms using stomata images taken with a different microscope and under different conditions (D), resulting in precision and recall accuracy of 99.72% and 96.88%, respectively. This indicates that the model built generalized well, in addition has high transferability. With the completion of this detection model, stomatal density measurement can be accelerated. This in turn will accelerate the breeding selection for drought tolerance. [ABSTRACT FROM AUTHOR]

Meiotic crossovers in outbred species, such as oil palm (Elaeis guineensis Jacq., 2n = 32) contribute to allelic re-assortment in the genome. Such genetic variation is usually exploited in breeding to combine positive alleles for trait superiority. A good quality reference genome is essential for identifying the genetic factors underlying traits of interest through linkage or association studies. At the moment, an AVROS pisifera genome is publicly available for oil palm. Distribution and frequency of crossovers throughout chromosomes in different origins of oil palm are still unclear. Hence, an ultrahigh-density genomic linkage map of a commercial Deli dura x AVROS pisifera family was constructed using the OP200K SNP array, to evaluate the genetic alignment with the genome assembly. A total of 27,890 linked SNP markers generated a total map length of 1,151.7 cM and an average mapping interval of 0.04 cM. Nineteen linkage groups represented 16 pseudo-chromosomes of oil palm, with 61.7% of the mapped SNPs present in the published genome. Meanwhile, the physical map was also successfully extended from 658 Mb to 969 Mb by assigning unplaced scaffolds to the pseudo-chromosomes. A genic linkage map with major representation of sugar and lipid biosynthesis pathways was subsequently built for future studies on oil related quantitative trait loci (QTL). This study improves the current physical genome of the commercial oil palm, and provides important insights into its recombination landscape, eventually unlocking the full potential genome sequence-enabled biology for oil palm. [ABSTRACT FROM AUTHOR]