New Serbian records of the slug Tandonia kusceri (H. Wagner, 1931) (Gastropoda: Eupulmonata: Milacidae) suggest its continuous dispersal to the north

Tandonia kusceri (H. Wagner) is a terrestrial slug native to the Balkan Peninsula (S. Serbia, N. Macedonia, Bulgaria and Dobrudja region of Romania) and the European part of Turkey. In Serbia, it was known mostly from the southern regions. The northernmost locality (Palić settlement, near Subotica) reported here suggests that the slug’s distribution is nearly continuous from its native range until Slovakia. The new record was first recognised from the pictures posted on a Facebook group for insect identification in 2021. The identification was subsequently confirmed by anatomical examination. This further emphasises the importance of social media in monitoring the spread of invasive invertebrates.

Computational Prediction of Disease Detection and Insect Identification using Xception model

In this paper, a detection tool has been built for the detection and identification of the diseases and pests found in the crops at its earliest stage. For this, various deep learning architectures were experimented to see which one of those would help in building a more accurate and an efficient detection model. The deep learning architectures used in this study were Convolutional Neural Network, VGG16, InceptionV3, and Xception. VGG16, InceptionV3, and Xception are categorized as the pre-trained models based on CNN architecture. They follow the concept of transfer learning. Transfer learning is a technique which makes use of the learnings of the models previously trained on a base data and applies it to the present dataset. This is an efficient technique which gives us rapid results and improved performance. Two plant datasets have been used here for disease and insects. The results of the algorithms were then compared. Most successful one has been the Xception model which obtained 82.89 for disease and 77.9 for pests.

Embedded System-Based Sticky Paper Trap with Deep Learning-Based Insect-Counting Algorithm

Flying insect detection, identification, and counting are the key components of agricultural pest management. Insect identification is also one of the most challenging tasks in agricultural image processing. With the aid of machine vision and machine learning, traditional (manual) identification and counting can be automated. To achieve this goal, a particular data acquisition device and an accurate insect recognition algorithm (model) is necessary. In this work, we propose a new embedded system-based insect trap with an OpenMV Cam H7 microcontroller board, which can be used anywhere in the field without any restrictions (AC power supply, WIFI coverage, human interaction, etc.). In addition, we also propose a deep learning-based insect-counting method where we offer solutions for problems such as the “lack of data” and “false insect detection”. By means of the proposed trap and insect-counting method, spraying (pest swarming) could then be accurately scheduled.

Insect Detection in Rice Crop using Google Code Lab

Herb plants are essential in the medical field today and can help humans. Phyllanthus Elegans Wall is used in this study to analyse and categorize whether it is a safe or unhealthy leaf. At the moment, most insect identification methods rely on physical classification, making it difficult to automatically, quickly, and reliably identify in stored grains. The concept of this research is to ascertain the quality of leaves by combining technology with pesticide classification in the agricultural sector. Picture collection, image processing, and classification are the first steps in enhancing leaf quality analysis. The segmentation using HSV to input RGB image for the colour alteration structure is the most significant image processing method for this section. The colour and shape of a leaf disease image are used to analyse it. Insect detection in complex backgrounds is more versatile with the score map that is decision alternate highly interconnected layer, and our detection speed has upgraded. Finally, the taxonomy approach employs an algorithm that feeds directly that employs formation backwards techniques. The result shows a Many-layer Preceptor and Nonlinear Activation Feature comparison, as well as a percentage of accuracy contrast between MLP and RBF. MLP and RBF are neural network algorithms. Clearly, the Neural Network classifier has a better presentation and precision.

Phytophagous insects and predatory arthropods in soybean and zinnia

Anggraini E, Anisa WN, Herlinda S, Irsan C, Suparman S, Suwandi S, Harun MU, Gunawan B. 2021. Phytophagous insects and predatory arthropods in soybean and zinnia. Biodiversitas 22: 1405-1414. In Indonesia, soybean is the third most important commodity after rice and corn. Cultivation technique such as planting hedgerows was expected to attract and retain natural enemies of pest predators. This study was conducted to determine the phytophagous insects and predatory arthropods species in soybean (Glycine max L.) and zinnia (Zinnia sp., hybrids) plants as refugia. This study utilized three methods i.e., visual observation, pitfall trap, net-trap in both soybean and zinnia planting areas. All collected insects were carried to the laboratory for observation and identification using insect determination key book. Insect identification was based on their morphological characteristics. The results study showed that phytophagous and predatory arthropods were found in both soybean and zinnia plantations in Agro-Techno Center (ATC), Sriwijaya University, Indonesia. There were 11 species of phytophagous and 5 species of entomophagous insects identified at soybean cultivation. Meanwhile, 5 species of phytophagous and 9 species of predatory arthropods including five spider species found, namely Pardosa distincta, Oxyopes javanus, Lycosa pseudomonas, Pardosa sp. and Drapetisca socialis at zinnia cultivation area. However, chi-square analysis (at alpha 0.05), confirmed that there was no significant difference between predatory arthropods in soybean and Zinnia the results indicate that soybean is might be suitable to be planted as refugia for predatory arthropods.