L’utilisation de systemes a liberation lente de semiochimiques dans les strategies de lutte integree. Le developpement des strategies de lutte integree est en croissance depuis que de nombreux problemes sont apparus suite a l’utilisation abusive et non raisonnee des pesticides de synthese. Les semiochimiques (molecules informatives utilisees dans les interactions insecte-insecte ou plante-insecte) sont de plus en plus consideres, au sein des strategies de lutte integree, comme des approches alternatives ou complementaires aux traitements insecticides. En effet, ces composes, specifiques a chaque espece, ne presentent pas d’effets negatifs relates dans la litterature envers les organismes benefiques et n’engendrent aucun risque de resistance chez les insectes ravageurs comme observes avec les insecticides. En raison de leur activite biologique complexe mais aussi de leur risque de degradation par les rayons ultraviolets ou a l’oxygene de l’air, leur dispersion dans l’environnement necessite l’elaboration de systemes garantissant une liberation lente et controlee des composes volatils actifs. Plusieurs etudes ont ete menees afin d’estimer le taux de liberation de systemes commercialises ou mis au point en laboratoire. L’influence des parametres climatiques et du type de diffuseur a ete estimee par plusieurs autres auteurs afin de fournir des indications sur la longevite des diffuseurs sur terrain. La presente revue analyse et critique une liste d’etudes de systemes a liberation lente.

/pdf/the-use-of-semiochemical-slow-release-devices-in-integrated-3u0kcsiy23.pdf

The use of semiochemical slow-release devices in integrated pest management strategies

The online automated maturity grading and counting of tomato fruits has a certain promoting effect on digital supervision of fruit growth status and unmanned precision operations during the planting process. The traditional grading and counting of tomato fruit maturity is mostly done manually, which is time-consuming and laborious work, and its precision depends on the accuracy of human eye observation. The combination of artificial intelligence and machine vision has to some extent solved this problem. In this work, firstly, a digital camera is used to obtain tomato fruit image datasets, taking into account factors such as occlusion and external light interference. Secondly, based on the tomato maturity grading task requirements, the MHSA attention mechanism is adopted to improve YOLOv8's backbone to enhance the network's ability to extract diverse features. The Precision, Recall, F1-score, and mAP50 of the tomato fruit maturity grading model constructed based on MHSA-YOLOv8 were 0.806, 0.807, 0.806, and 0.864, respectively, which improved the performance of the model with a slight increase in model size. Finally, thanks to the excellent performance of MHSA-YOLOv8, the Precision, Recall, F1-score, and mAP50 of the constructed counting models were 0.990, 0.960, 0.975, and 0.916, respectively. The tomato maturity grading and counting model constructed in this study is not only suitable for online detection but also for offline detection, which greatly helps to improve the harvesting and grading efficiency of tomato growers. The main innovations of this study are summarized as follows: (1) a tomato maturity grading and counting dataset collected from actual production scenarios was constructed; (2) considering the complexity of the environment, this study proposes a new object detection method, MHSA-YOLOv8, and constructs tomato maturity grading models and counting models, respectively; (3) the models constructed in this study are not only suitable for online grading and counting but also for offline grading and counting. 

Tomato Maturity Detection and Counting Model Based on MHSA-YOLOv8

Camellia oleifera fruits are randomly distributed in an orchard, and the fruits are easily blocked or covered by leaves. In addition, the colors of leaves and fruits are alike, and flowers and fruits grow at the same time, presenting many ambiguities. The large shock force will cause flowers to fall and affect the yield. As a result, accurate positioning becomes a difficult problem for robot picking. Therefore, studying target recognition and localization of Camellia oleifera fruits in complex environments has many difficulties. In this paper, a fusion method of deep learning based on visual perception and image processing is proposed to adaptively and actively locate fruit recognition and picking points for Camellia oleifera fruits. First, to adapt to the target classification and recognition of complex scenes in the field, the parameters of the You Only Live Once v7 (YOLOv7) model were optimized and selected to achieve Camellia oleifera fruits’ detection and determine the center point of the fruit recognition frame. Then, image processing and a geometric algorithm are used to process the image, segment, and determine the morphology of the fruit, extract the centroid of the outline of Camellia oleifera fruit, and then analyze the position deviation of its centroid point and the center point in the YOLO recognition frame. The frontlighting, backlight, partial occlusion, and other test conditions for the perceptual recognition processing were validated with several experiments. The results demonstrate that the precision of YOLOv7 is close to that of YOLOv5s, and the mean average precision of YOLOv7 is higher than that of YOLOv5s. For some occluded Camellia oleifera fruits, the YOLOv7 algorithm is better than the YOLOv5s algorithm, which improves the detection accuracy of Camellia oleifera fruits. The contour of Camellia oleifera fruits can be extracted entirely via image processing. The average position deviation between the centroid point of the image extraction and the center point of the YOLO recognition frame is 2.86 pixels; thus, the center point of the YOLO recognition frame is approximately considered to be consistent with the centroid point of the image extraction.

/pdf/adaptive-active-positioning-of-camellia-oleifera-fruit-1k3wejx1.pdf

Adaptive Active Positioning of Camellia oleifera Fruit Picking Points: Classical Image Processing and YOLOv7 Fusion Algorithm

The box tree pyralid, Glyphodes perspectalis, is the most destructive pest of the box tree in Korea and was recently introduced into Europe. The previously known as EAG active components of this moth, (Z)-11-hexadecenal (Z11-16:Ald), (E)-11-hexadecenal (E11-16:Ald), and (Z)-11-hexadecenol (Z11-16:OH) have been detected from the extracts of female abdomen. The ratios of these three compounds identified in female moth were 5.2:1:0.2 in 2010 and 6.5:1:0.2 in 2011. During field bioassays, it was found that the male moths were not attracted to Z11-16:Ald or E11-16:Ald when used alone; however, they were attracted to a mixture of the above. The most effective ratios of Z11-16:Ald to E11-16:Ald were 5:1 and 7:1. A small amount of Z11-16:OH inhibited male moth attraction in field bioassays. Further, a uni-trap was found to be more effective in catching the moth than delta and wing traps were. In field bioassays using 2 different types of lures, significantly more male G. perspectalis were caught to film-type lures (50.5 ± 4.4/trap) than those to rubber septum lures (35.8 ± 5.2/trap).

Female sex pheromone components of the box tree pyralid, Glyphodes perspectalis, in Korea: Field test and development of film-type lure

The potential use of polyurea microcapsules, as ‘release carriers’ for insect pheromones, has been demonstrated. (Z)-11-hexadecenyl acetate (Z11-16:Ac), the major sex pheromone component of several Noctuidae species, was used as the model molecule. The coating material's ability to release the pheromone was initially studied by the solid-phase micro-extraction technique. Polyurea microcapsules released Z11-16:Ac relatively slowly, with a duration of approximately 1 month, as it was determined under both laboratory and semi-field conditions. Preliminary laboratory bioassays revealed a satisfactory attraction of Sesamia males, at doses of 50 and 500 mg of dried microcapsules containing the aforementioned pheromone. Almost all male insects tested initiated flight and among them 40.2–49.4% successfully contacted the pheromone source. The preparation of polyurea microcapsules needs further refinement as to increase release duration; nevertheless, these results demonstrate strong potential for the future use of polyurea microcapsules as part of integrated insect control programmes.

Prolonged slow release of (Z)-11-hexadecenyl acetate employing polyurea microcapsules

This study looked at the development of six deep learning artificial neural network models for detecting ripeness stage in wild blueberries, along with developing models for yield estimation. The six networks used were YOLOv3, YOLOv3-SPP, YOLOv3-Tiny, YOLOv4, YOLOv4-Small and YOLOv4-Tiny. Both 3-class (green berries, red berries, blue berries) and 2-class (unripe berries, ripe berries) models were developed with YOLOv4 performing the best with mean average precisions of 79.79% and 88.12% respectively. This result was further supported by YOLOv4 achieving the highest F1 score of 0.82. YOLOv4-Tiny performed the best from a computational load perspective having a mean inference time of 7.8 ms and a mean memory usage of 1.63 GB for single 1280 × 736 pixel images. Only minor differences in the accuracy of the nonlinear regression yield prediction models were detected, with YOLOv4-Small performing the best with a mean absolute error of 24.1%. Despite this error, the results are encouraging, and this novel approach to yield estimation in wild blueberries will aid growers in making better, more localized, management decisions, improving yields and ultimately increasing profits by better understanding their fields ripening characteristics. 

Detection of Fruit Maturity Stage and Yield Estimation in Wild Blueberry Using Deep Learning Convolutional Neural Networks

The potential utility of micrometer-sized particles as controlled-release devices for the volatilization of insect pheromones for mating disruption applications is evaluated in this study for two pheromone/model compound systems (codlemone/1-dodecanol and disparlure/1,2-epoxyoctadecane). To expedite the measurement of release rates from these particle devices, two techniques based on thermogravimetric analysis (TGA) have been exploited:  isothermal TGA (I-TGA) at elevated temperatures (40−80 °C) with N2 convection and volatilization temperature (VT) by dynamic TGA. A correlation between these two methods has been established. Samples that exhibit a higher VT provide a lower release rate from a particle substrate. Using these techniques, it has been demonstrated that chemical interactions between adsorbed liquids and particle surfaces may play a small role in defining release characteristics under conditions of low surface area, whereas parameters associated with total surface area and micropore structure ...

Microparticle dispensers for the controlled release of insect pheromones

An operator of a wild blueberry harvester faces the fatigue of manually adjusting the height of the harvester's head, considering spatial variations in plant height, fruit zone, and field topography affecting fruit yield. For stress-free harvesting of wild blueberries, a deep learning-supported machine vision control system has been developed to detect the fruit height and precisely auto-adjust the header picking teeth rake position. The OpenCV AI Kit (OAK-D) was used with YOLOv4-tiny deep learning model with code developed in Python to solve the challenge of matching fruit heights with the harvester's head position. The system accuracy was statistically evaluated with R2 (coefficient of determination) and σ (standard deviation) measured on the difference in distances between the berries picking teeth and average fruit heights, which were 72, 43% and 2.1, 2.3 cm for the auto and manual head adjustment systems, respectively. This innovative system performed well in weed-free areas but requires further work to operate in weedy sections of the fields. Benefits of using this system include automated control of the harvester's head to match the header picking rake height to the level of the fruit height while reducing the operator's stress by creating safer working environments. 

/pdf/deep-learning-supported-machine-vision-system-to-precisely-sxl3wpxn.pdf

Deep learning supported machine vision system to precisely automate the wild blueberry harvester header

Improving fruit quality is important to wild blueberry producers. Plant debris like leaves and stems caused a reduction in fruit quality during mechanical harvesting. A debris detection system was developed using two Logitech C920 webcam cameras mounted before and after the blower fan on a commercial mechanical wild blueberry harvester. Images were collected from two commercially managed wild blueberry fields located in central Nova Scotia to develop a dataset of 1000 images. Two convolutional neural networks (CNNs), YOLOv3 and YOLOv3-SPP, were used and compared for performance analysis. The CNNs were trained using no augmented images, images with five augmentations (sharpening, brightness, contrast, gamma correction, and saturation), and images with five augmentations that were more heavily weighted towards gamma. Four different computer hardware packages (Hewlett-Packard, Shuttle XPC, Jetson TX2, and GPU-based Desktop) were used to determine the appropriate hardware and CNN model combination for real-time performance. YOLOv3 and YOLOv3-SPP were trained to mAP scores of 72.87% and 74.38% using the dataset with more gamma-augmented images. This was an improvement over the dataset with no augmentation (67.75%; 68.49%) and equally numbered augmentations (71.26%; 73.03%). YOLOv3-SPP combined with an Intel® Core™ i9-7900X CPU and Nvidia GeForce RTX™ 2080 Ti GPU in a desktop computer achieved the fastest detection rate of 33.30 ms. The detection speed on all other hardware configurations exceeded 833.33 ms, indicating that a powerful desktop GPU is required for real-time performance of this task. With adequate processing hardware, the developed technology could be integrated into a control system to automatically adjust brushes based on conveyor information from mechanical wild blueberry harvesters to make cleaning berries easier and enhance fruit quality. 

Machine vision system for real-time debris detection on mechanical wild blueberry harvesters

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Detection of Fruit Maturity Stage and Yield Estimation in Wild Blueberry Using Deep Learning Convolutional Neural Networks

Microparticle dispensers for the controlled release of insect pheromones

Deep learning supported machine vision system to precisely automate the wild blueberry harvester header

Machine vision system for real-time debris detection on mechanical wild blueberry harvesters