Automated vision-based system for monitoring Asian citrus psyllid in orchards utilizing artificial intelligence

Highlights

• An automated system for monitoring ACP in groves was developed.

• This low-cost technology can distinguish target insects from other insects and objects.

• Neural networks were utilized for ACP detection and classification.

• It develops a map to visualize the ACP numbers for each tree.

• It can automate scouting procedures in citrus and to be extended to other crop insects.

Abstract

Specialty crop growers face challenges from numerous diseases and pests. For example, the Asian citrus psyllid (ACP) is a key pest of citrus due to its role as vector of huanglongbing (HLB) (greening disease). There is no known cure for HLB, but vector management is critical, both for slowing spread and attenuating symptoms in infected trees. Therefore, monitoring ACP population, as well as other pest populations, is an essential management component for timing and assessment of control actions. Manual crop scouting is often labor intensive and time consuming. In this work, an automated system was developed and evaluated utilizing machine vision and artificial intelligence to monitor ACP in groves. This system comprised a tapping mechanism to collect insects from the tree’s branches and a board with a grid of cameras for image acquisition. A software was developed using two convolutional neural-networks to accurately detect and distinguish psyllids from other insects and debris fallen from the tree. A GPS was utilized to automatically record individual tree position to facilitate data assessment on large groves. A precision and recall of 80% and 95%, respectively, was obtained on detecting ACPs on a sample of 90 young citrus trees. The system proved a great potential to automate scouting procedures in citrus and to be extended to other crop insects.

Introduction

The Huanglongbing (HLB), or citrus greening, is a significant disease that affects citrus orchards causing rapid decline of the trees (Chung and Brlansky, 2005). HLB was first reported in 1919 in southern China, and it is spread already to 40 different countries all around the world, including the USA (Bové, 2006). The disease causes an immunologic resistance reduction affecting orange production by causing fruit drop and the production of small, misshapen and low-level of juice fruit with no economic value (Hodges and Spreen, 2012, Albrecht and Bowman, 2009). The causal agent of HLB in the America and Asia is usually the Candidatus Liberibacter asiaticus (CLas), a phloem limited gram-negative bacterium (Jagoueixet al., 1994, Garnier and Bové, 1996). In 17 years (between 2000 and 2017), the citrus cultivated area in Florida declined 45% and the volume production utilized declined by 71%, mainly due to problems generated by the HLB (Court et al., 2018). Between 2012 and 2017 in Florida, citrus production revenue decreased approximately 47% (from approximately 2.1 billion dollars to approximately 1.2 billion dollars) (Court et al., 2018), resulting in an average annual reduction of 7945 jobs and $1.098 billion in industry output (Court et al., 2017).

The HLB is transmitted by the vector Diaphorina citri Kuwayama, most know as Asian Citrus Psyllid (ACP) (Hemiptera: Phyllidae). It was verified in Florida for the first time in 1998 (Halbert and Manjunath, 2004). The transmission of the disease occurs in three steps: (i) an initial period of acquisition, when the nymphs and the adults come in contact with the pathogenic agent (CLas), (ii) a latency period, that may include a bacteria reproduction too (Inoue et al., 2009) and (iii) the inoculation period, where the vector transmits the bacteria (presents in the salivary glandules, muscles, tissue and ovaries) to the plant (Ammaret al., 2011, Ammaret al., 2010). The ACP can move about 100 m in 3 days (Boina et al., 2009) and recent data showed a dispersion distance of at least 2 km in 12 days (Lewis-Rosenblum, 2011). This vector exhibits biological characteristics such as high reproduction capacity, fast population growth ratio and capacity to withstand a wide temperature range, which allows it to spread quickly (Halbert and Manjunath, 2004). Bacteria control is becoming virtually unsustainable as robust methods of bacteria elimination are expensive and not effective (Halbert and Manjunath, 2004). Therefore methods for monitoring and controlling the ACP populations are shown to be more advantageous in the prevention and mitigation of the citrus greening problem, allowing the treatment only in critical plants, saving time and reducing costs (Halbert and Manjunath, 2004).

A method to combat the ACP vector includes extensive chemical control programs, by the application of pesticides. However, empiric studies are showing that pesticides application to prevent the introduction and dissemination of ACP has not been very effective (Gottwald, 2007). Besides that, ACP populations have become more resistant to chemicals (Tiwari et al., 2011), which can become problematic without controlled use of pesticides. The use of biological agents as natural predators of the vector is another method of population control (Hall, 2013), in which case chemicals can reduce the effectiveness of the method (Halbert and Manjunath, 2004). Fungi control (entomopathogenic fungi) was also reported as a good tool to control ACP populations (Hallet al., 2013, Moranet al., 2011, Samson, 1974, Subandiyahet al., 2000). All the mentioned control methods should be supported by a strong geolocation analysis of ACP quantities, identifying most affected areas and generating accurate targets, both to chemical and biological control of the vector.

Monitoring the ACP population is an essential component of ACP management, both for application of economic thresholds as well as assessing effectiveness of control actions (Monzo and Stansly, 2017). For this purpose, the tap sample method (other traditional ACP monitoring methods are presented below), which requires striking a randomly selected branch and counting ACP falling onto a sheet, has proven to be an efficient and reliable tool for assessing ACP numbers in the tree canopy (Hall and Hentz, 2010, Monzoet al., 2015). Spraying based on need as indicated by tap sample counts has been shown to reduce ACP management costs and conserve natural enemies (Monzo et al., 2014, Monzo and Stansly, 2017). However, this tap sample manual counting method is very labor intensive and time consuming.

Since labor shortage is a major issue in USA, machine vision techniques, internet of things (IoT) (Ampatzidiset al., 2018, Ampatzidiset al., 2012) and cloud-based technologies (Ampatzidis et al., 2016) can simplify pest scouting procedures and improve precision spraying applications (Partel et al., 2019), reduce labor cost, decrease data collection time, and produce critical and practical information (Ampatzidis et al., 2017, Luvisi et al., 2016). Rapid methods for early detection of pests and diseases would assist growers in making timely management decisions and to limit spread (Abdulridhaet al., 2019, Abdulridha et al., 2018).

Machine vision along with artificial intelligence (AI) techniques has been increasingly applied to agriculture (Cruz et al., 2017). Moller (2010) concluded that using computer vision technologies on agricultural operations lowers the operator stress levels (Moller, 2010). Deep learning based convolutional neural networks (CNN) is the most common AI approach for image recognition and have proven to achieve great performance on image detection and classification tasks (Krizhevskyet al., 2012, Güçlü and Van, 2014, Cruzet al., 2019). Their deep architecture and good weight equalization schemes provide great sensitivity to detect complex and high level features. CNN’s have the advantage to be trained from large sets of data, eliminating the need to manually design feature extraction algorithms. In this study, a novel artificial intelligence technology utilizing deep learning was developed and evaluated to automate ACP scouting procedures in citrus orchards, providing rapid and valuable information to improve ACP management.