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Counting planthoppers manually is laborious and yields inconsistent results, particularly when dealing with species with similar features, such as the brown planthopper (Nilaparvata lugens; BPH), whitebacked planthopper (Sogatella furcifera; WBPH), zigzag leafhopper (Maiestas dorsalis; ZIGZAG), and green leafhopper (Nephotettix malayanus and Nephotettix virescens; GLH). Most of the available automated counting methods are limited to populations of a small density and often do not consider those with a high density, which require more complex solutions due to overlapping objects. Therefore, this research presents a comprehensive assessment of an object detection algorithm specifically developed to precisely detect and quantify planthoppers. It utilises annotated datasets obtained from sticky light traps, comprising 1654 images across four distinct classes of planthoppers and one class of benign insects. The datasets were subjected to data augmentation and utilised to train four convolutional object detection models based on transfer learning. The results indicated that Faster R-CNN VGG 16 outperformed other models, achieving a mean average precision (mAP) score of 97.69% and exhibiting exceptional accuracy in classifying all planthopper categories. The correctness of the model was verified by entomologists, who confirmed a classification and counting accuracy rate of 98.84%. Nevertheless, the model fails to recognise certain samples because of the high density of the population and the significant overlap among them. This research effectively resolved the issue of low- to medium-density samples by achieving very precise and rapid detection and counting. [ABSTRACT FROM AUTHOR]

Basal Stem Rot (BSR), a disease caused by Ganoderma boninense (G. boninense), has posed a significant concern for the oil palm industry, particularly in Southeast Asia, as it has the potential to cause substantial economic losses. The breeding programme is currently searching for G. boninense-resistant planting materials, which has necessitated intense manual screening in the nursery to track the progression of disease development in response to different treatments. The combination of hyperspectral image and machine learning approaches has a high detection potential for BSR. However, manual feature selection is still required to construct a detection model. Therefore, the objective of this study is to establish an automatic BSR detection at the seedling stage using a pre-trained deep learning model and hyperspectral images. The aerial view image of an oil palm seedling is divided into three regions in order to determine if there is any substantial spectral change across leaf positions. To investigate if the background images affect the performance of the detection, segmented images of the plant seedling have been automatically generated using a Mask Region-based Convolutional Neural Network (RCNN). Consequently, three models are utilised to detect BSR: a convolutional neural network that is 16 layers deep (VGG16) model trained on a segmented image; and VGG16 and Mask RCNN models both trained on the original images. The results indicate that the VGG16 model trained with the original images at 938 nm wavelength performed the best in terms of accuracy (91.93%), precision (94.32%), recall (89.26%), and F1 score (91.72%). This method revealed that users may detect BSR automatically without having to manually extract image attributes before detection. [ABSTRACT FROM AUTHOR]

Basal stem rot (BSR) is a type of disease that induces oil palm death within a short span of the appearance of symptoms. BSR early detection would facilitate to curb this by adopting appropriate strategies. In this paper, a systematic review was undertaken to demonstrate the need for authentic health condition monitoring of oil palm plantations. The currently used remotely sensed (RS) techniques for BSR detection and classification were reviewed. Several kinds of RS techniques were exerted for BSR detection and its severity classification up to four levels. It was identified that applied geospatial technologies, including multispectral and hyperspectral remote sensing, terrestrial laser scanning, spatial maps, tomography images, intelligent e-nose and Microfocus X-ray fluorescence, were capable of distinguishing infected oil palms from the non-infected ones. Furthermore, some of them are able to categorize BSR severity level up to four levels as well as of its early detection. [ABSTRACT FROM AUTHOR]

This study compared the effect of application of edible coating on or before ultraviolet treatment on postharvest longan fruits. The treated longan fruits were examined for weight loss, respiration rate, surface color changes, enzymatic activities (PPO, POD, and PAL), and total phenolic contents throughout the 7 storage days at ambient temperature. In addition, coat homogeneity was examined and cell structure of longan flesh at the end of storage was observed. The results showed that when UV was applied before coating (i.e., chitosan or carrageenan), it had relatively lower PPO and PAL activities and retained higher TPC in longan pericarp. However, the changes in enzymatic activities did not affect the surface lightness and browning index as they were more influenced by the type of coating, in which combination treatments with carrageenan showed higher surface lightness and lower browning index compared to treatment combinations containing chitosan. However, whenUVtreatment preceded coating, the combinations of UV plus chitosan coating produced lower PPO and PAL activities and retained better cell structure with less damage than the combinations of UV plus carrageenan coating. UV plus carrageenan coating showed relatively higher weight loss and respiration rate, with cell structure exhibiting bigger intercellular spaces at the end of storage. Therefore, application of UV treatment followed by chitosan coating was found to be the best treatment combination for controlling enzymatic activities and reducing senescence rate of longan fruits. [ABSTRACT FROM AUTHOR]