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1. Automatic image segmentation method for cotton leaves with disease under natural environment

ZHANG Jian-hua, KONG Fan-tao, WU Jian-zhai, HAN Shu-qing, ZHAI Zhi-fen

Journal of Integrative Agriculture 2018, 17 (08): 1800-1814. DOI: 10.1016/S2095-3119(18)61915-X

摘要（353）

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Received 25 July, 2017 Accepted 19 February, 2018

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2.

MmNet: Identifying Mikania micrantha Kunth in the wild via a deep Convolutional Neural Network

QIAO Xi, LI Yan-zhou, SU Guang-yuan, TIAN Hong-kun, ZHANG Shuo, SUN Zhong-yu, YANG Long, WAN Fang-hao, QIAN Wan-qiang

Journal of Integrative Agriculture 2020, 19 (5): 1292-1300. DOI: 10.1016/S2095-3119(19)62829-7

摘要（156）

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Mikania micrantha Kunth is an invasive alien weed and known as a plant killer around the world. Accurately and rapidly identifying M. micrantha in the wild is important for monitoring its growth status, as this helps management officials to take the necessary steps to devise a comprehensive strategy to control the invasive weed in the identified area. However, this approach still mainly depends on satellite remote sensing and manual inspection. The cost is high and the accuracy rate and efficiency are low. We acquired color images of the monitoring area in the wild environment using an Unmanned Aerial Vehicle (UAV) and proposed a novel network -MmNet- based on a deep Convolutional Neural Network (CNN) to identify M. micrantha in the images. The network consists of AlexNet Local Response Normalization (LRN), along with the GoogLeNet and continuous convolution of VGG inception models. After training and testing, the identification of 400 testing samples by MmNet is very good, with accuracy of 94.50% and time cost of 10.369 s. Moreover, in quantitative comparative analysis, the proposed MmNet not only has high accuracy and efficiency but also simple construction and outstanding repeatability. Compared with recently popular CNNs, MmNet is more suitable for the identification of M. micrantha in the wild. However, to meet the challenge of wild environments, more M. micrantha images need to be acquired for MmNet training. In addition, the classification labels need to be sorted in more detail. Altogether, this research provides some theoretical and scientific basis for the development of intelligent monitoring and early warning systems for M. micrantha and other invasive species.

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3.

Using an image segmentation and support vector machine method for identifying two locust species and instars

Shuhan LU, YE Si-jing

Journal of Integrative Agriculture 2020, 19 (5): 1301-1313. DOI: 10.1016/S2095-3119(19)62865-0

摘要（91）

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Locusts are agricultural pests around the world. To cognize how locust distribution density and community structure are related to the hydrothermal and vegetation growth conditions of their habitats and thereby providing rapid and accurate warning of locust invasions, it is important to develop efficient and accurate techniques for acquiring locust information. In this paper, by analyzing the differences between the morphological features of Locusta migratoria manilensis and Oedaleus decorus asiaticus, we proposed a semi-automatic locust species and instar information detection model based on locust image segmentation, locust feature variable extraction and support vector machine (SVM) classification. And we subsequently examined its applicability and accuracy based on sample image data acquired in the field. Locust image segmentation experiment showed that the proposed GrabCut-based interactive segmentation method can be used to rapidly extract images of various locust body parts and exhibits excellent operability. In a locust feature variable extraction experiment, the textural, color and morphological features of various locust body parts were calculated. Based on the results, eight feature variables were selected to identify locust species and instars using outlier detection, variable function calculation and principal component analysis. An SVM-based locust classification experiment achieved a semi-automatic detection accuracy of 96.16% when a polynomial kernel function with a penalty factor parameter c of 2 040 and a gamma parameter g of 0.5 was used. The proposed detection model exhibits advantages such as high applicability and accuracy when it is used to identify locust instars of L. migratoria manilensis and O. decorus asiaticus, and it can also be used to identify other species of locusts.

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4.

Predicting the potential geographic distribution of Bactrocera bryoniae and Bactrocera neohumeralis (Diptera: Tephritidae) in China using MaxEnt ecological niche modeling

Jing Wan, QI Guo-jun, MA Jun, Yonglin Ren, WANG Rui, Simon MCKIRDY

Journal of Integrative Agriculture 2020, 19 (8): 2072-2082. DOI: 10.1016/S2095-3119(19)62840-6

摘要（163）

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Bactrocera bryoniae and Bactrocera neohumeralis are highly destructive and major biosecurity/quarantine pests of fruit and vegetable in the tropical and subtropical regions in the South Pacific and Australia. Although these pests have not established in China, precautions must be taken due to their highly destructive nature. Thus, we predicted the potential geographic distribution of B.?bryoniae and B. neohumeralis across the world and in particular China by ecological niche modeling of the Maximum Entropy (MaxEnt) model with the occurrence records of these two species. Bactrocera bryoniae and B. neohumeralis exhibit similar potential geographic distribution ranges across the world and in China, and each species was predicted to be able to distribute to over 20% of the globe. Globally, the potential geographic distribution ranges for these two fruit fly species included southern Asia, the central and the southeast coast of Africa, southern North America, northern and central South America, and Australia. While within China, most of the southern Yangtze River area was found suitable for these species. Notably, southern China was considered to have the highest risk of B. bryoniae and B. neohumeralis invasions. Our study identifies the regions at high risk for potential establishment of B. bryoniae and B. neohumeralis in the world and in particular China, and informs the development of inspection and biosecurity/quarantine measures to prevent and control their invasions.

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5. Development of an automatic monitoring system for rice light-trap pests based on machine vision

YAO Qing, FENG Jin, TANG Jian, XU Wei-gen, ZHU Xu-hua, YANG Bao-jun, Lü Jun, XIE Yi-ze, YAO Bo, WU Shu-zhen, KUAI Nai-yang, WANG Li-jun

Journal of Integrative Agriculture 2020, 19 (10): 2500-2513. DOI: 10.1016/S2095-3119(20)63168-9

摘要（104）

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Monitoring pest populations in paddy fields is important to effectively implement integrated pest management. Light traps are widely used to monitor field pests all over the world. Most conventional light traps still involve manual identification of target pests from lots of trapped insects, which is time-consuming, labor-intensive and error-prone, especially in pest peak periods. In this paper, we developed an automatic monitoring system for rice light-trap pests based on machine vision. This system is composed of an intelligent light trap, a computer or mobile phone client platform and a cloud server. The light trap firstly traps, kills and disperses insects, then collects images of trapped insects and sends each image to the cloud server. Five target pests in images are automatically identified and counted by pest identification models loaded in the server. To avoid light-trap insects piling up, a vibration plate and a moving rotation conveyor belt are adopted to disperse these trapped insects. There was a close correlation (r=0.92) between our automatic and manual identification methods based on the daily pest number of one-year images from one light trap. Field experiments demonstrated the effectiveness and accuracy of our automatic light trap monitoring system.

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6. 快速，低成本的深度学习系统可基于云服务对草莓病害进行分类

YANG Guo-feng, YANG Yong, HE Zi-kang, ZHANG Xin-yu, HE Yong

Journal of Integrative Agriculture 2022, 21 (2): 460-473. DOI: 10.1016/S2095-3119(21)63604-3

摘要（179）

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准确、及时地对草莓种植过程中的病害进行分类，可以帮助种植者对其进行及时的处理，从而减少损失。但真实种植环境下的草莓病害分类面临着严峻的挑战，包括复杂的种植环境，多种差异较小的病害类别等。尽管最近基于深度学习的移动视觉技术在克服上述问题方面取得了一些成功，但对多地域、多空间、多时间的草莓病害分类需求而言，一个关键问题是如何构建一种无损、快速、便捷的方法提高草莓病害识别的效率。我们开发并评估一种快速，低成本的系统，用于对草莓种植中的病害进行分类。这涉及设计一个易于使用的基于云服务的草莓病害识别系统，并结合我们提出的新颖的自监督多网络协作的分类模型，它由定位网络、反馈网络和分类网络组成，以识别草莓常见病害的类别。该模型借助新颖的自我监督机制，可以有效地识别草莓病害图像中的病害区域，而不需要边界框等标注。使用准确率，精确率，召回率和值来评估分类效果，测试集的结果分别为92.48％，90.68％，86.32%和88.45%。与流行的卷积神经网络和其他五种方法相比，该网络实现了更好的病害分类效果。目前，系统的客户端（小程序）已上线微信平台。小程序在实际测试中分类效果良好，验证了系统的可行性和有效性，能够为草莓病害识别的智能化研究与应用提供参考。

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7. Intelligent diagnosis of northern corn leaf blight with deep learning model

PAN Shuai-qun, QIAO Jing-fen, WANG Rui, YU Hui-lin, WANG Cheng, Kerry TAYLOR, PAN Hong-yu

Journal of Integrative Agriculture 2022, 21 (4): 1094-1105. DOI: 10.1016/S2095-3119(21)63707-3

摘要（213）

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Maize (Zea mays L.), also known as corn, is the third most cultivated crop in the world. Northern corn leaf blight (NCLB) is a globally devastating maize foliar disease caused by Setosphaeria turcica (Luttrell) Leonard and Suggs. Early intelligent diagnosis and warning is an effective and economical strategy to control this disease. Today, deep learning is beginning to play an essential role in agriculture. Notably, deep convolutional neural networks (DCNN) are amongst the most successful machine learning techniques in plant disease detection and diagnosis. Our study aims to identify NCLB in the maize-producing area in Jilin Province based on several DCNN models. We established a database of 985 leaf images of healthy and infected maize and applied data augmentation techniques including image segmentation, image resizing, image cropping, and image transformation, to expand to 30 655 images. Several proven convolutional neural networks, such as AlexNet, GoogleNet, VGG16, and VGG19, were then used to identify diseases. Based on the best performance of the DCNN pre-trained model GoogleNet, some of the recent loss functions developed for deep facial recognition tasks such as ArcFace, CosFace, and A-Softmax were applied to detect NCLB. We found that a pre-trained GoogleNet architecture with the Softmax loss function can achieve an excellent accuracy of 99.94% on NCLB diagnosis. The analysis was implemented in Python with two deep learning frameworks, Pytorch and Keras. The techniques, training, validation, and test results are presented in this paper. Overall, our study explores intelligent identification technology for NCLB and effectively diagnoses NCLB from images of maize.

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