Use of Artificial Intelligence (AI) in Agricultural Pest Management

Artificial Intelligence (AI) is the technology that enables machines to carry out tasks like learning, reasoning, problem-solving, and decision-making that normally need human intelligence. In essence, artificial intelligence (AI) allows computers to mimic human cognitive processes and make judgments without the need for explicit programming. Increased use of artificial intelligence (AI) in pest management is improving pest identification, forecasting, and treatment, resulting in more effective and sustainable methods. By evaluating data from multiple sources, such as cameras and sensors, AI-powered solutions expedite pest detection and allow for the accurate and quick identification of infestations. Additionally, this data analysis aids in forecasting pest outbreaks and suggesting the best control strategies.
To achieve precision control over crop pests and diseases, fertilization, and irrigation in the farm field, smart agriculture has recently been introduced to apply artificial intelligence (AI) techniques, information, and wireless communication technologies, such as the Internet of Things (IoT), in all aspects of agriculture. This integration lowers labour and material expenses while increasing pest control effectiveness. The primary use of smart agriculture is thought to be crop health monitoring, which establishes the current state of the farm in relation to plant pests. However, due to the intricate structure and high degree of resemblance among insect pests, farmers find it difficult to classify agricultural pests. However, farmers can use the right insecticides to stop the spread of these insects if they identify plant pests early in the infection process. To monitor, track, and employ these agricultural inputs at the best times, it has become clear that artificial intelligence (AI) algorithms are essential. Therefore, combining entomology and artificial intelligence will aid in the prompt and efficient management and prediction of diseases and pests. AI systems can detect early indications of pest infestations by analysing data from multiple sources, such as drones, satellite photography, and ground sensors. These models notify users of the existence of pests or diseases before symptoms appear by using machine learning (ML) to spot minute patterns and anomalies in the crops. For crop protection and pest identification, farmers frequently employ DL techniques like CNN, DCNN, Long Short-Term Memory (LSTM), and Deep Belief Network (DBN) in addition to ML tools like Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF).
Tamil Nadu Agricultural University (TNAU) recently created an Android smartphone application as part of integrated pest management (IPM) package to detect fall armyworm infection in maize crops. The application uses Deep Convolutional Neural Networks (DCNNs) and transfer learning to detect contaminated areas with high accuracy, achieving 98.47% training accuracy and 93.47% validation accuracy. The software uses artificial intelligence (AI) algorithms to detect fall armyworm infestations early, allowing for quick intervention measures like targeted pesticide application or integrated pest management plans. This digital approach demonstrates the promise of innovative technologies in tackling crop pest concerns by enabling farmers to efficiently preserve maize harvests and livelihoods. AI-enabled drones equipped with high-definition cameras and sensors can accurately survey agricultural or wooded regions. Computer vision algorithms analyse the data to find pests in images, assess crop health, and indicate areas of concern; this information allows for targeted treatments and resource allocation. AI reduces the use of pesticides and their negative effects on the environment by analysing data on crop health, pest dispersal, and environmental variables. Because grape moths pose a hazard to wine production in vineyards, pheromone traps are used for pest management and monitoring. Because of their effectiveness, smart pest monitoring systems that combine cameras, sensors, and artificial intelligence are becoming more and more popular.
AI integrates a range of data, including pest life cycles, agricultural phenology, and weather patterns, to provide prediction models for pest forecasting. These models employ both real-time inputs and historical data to forecast insect outbreaks. This makes it possible to respond quickly and use proactive management strategies. For accurate and quick identification and monitoring of the incidence of pests and diseases, data on insect populations, motions, and behavior is obtained using IoT cameras and sensors. Farmers deal with the complex interactions between environmental factors, pest dynamics, and overuse of pesticides. Researchers have developed an IPM platform that uses AI, data analytics, and a variety of data sources to address these issues. By optimizing agricultural pest management methods, this creative solution seeks to reduce environmental harm and the emergence of pesticide resistance while providing farmers with more sustainable and efficient crop protection strategies. AI-powered gadgets reduce labor costs and increase crop output by automating pest monitoring and identification. Autonomous robots with sensors can move across woods or fields, gathering information and locating pest hotspots to allow for real-time decision-making and targeted interventions. Additionally, by fusing different management techniques, like biological control and cultural norms, AI enhances IPM solutions. In order to suggest the most effective and sustainable pest management techniques, AI systems examine a variety of data sources.
An automatic technique for identifying tiny insect pests in greenhouses using images of sticky paper traps was invented by Rustia and her team. To identify and categorize insect things, their cascaded technique uses image classifiers and a CNN object detector. With mean counting precisions of 0.90 and 0.91 on different datasets and average F1-scores of 0.92 and 0.90, the algorithm demonstrated great accuracy when tested in a variety of greenhouse conditions. By providing timely information on insect pest incidences, this automated technology helps agriculture implement more effective IPM tactics. With a 93.78% accuracy rate in identifying plant pests, the GoogleNet CNN model enables farmers to use smartphone applications to identify diseases and pests on their crop fields. With the use of various algorithms, AI is getting increasingly sophisticated in the detection, identification, and control of pests. Similar to this, Trimble’s Farmer Core software analyzes farm data, such as soil samples, weather trends, and crop health photos, using AI and machine learning to provide farmers with useful information for increasing crop yields and productivity. Farmers may make data-driven decisions with Taranis, an AI-powered precision agriculture platform that leverages weather data, satellite imaging, and machine learning algorithms to identify pest infestations, crop diseases, and nutritional deficits in horticulture crops. In order to monitor forest health, identify deforestation, and evaluate biodiversity, Descartes Labs’ AI-based platform evaluates satellite imagery and other geospatial data, assisting conservationists and forest managers in making well-informed decisions. By combining AI and IoT technology, City Zenith’s SmartWorldPro platform keeps an eye out for pest infestations like rats and mosquitoes in metropolitan areas. It then gives city officials real-time data so they may implement tailored pest control measures. The Debug Project, created by Alphabet business Verily, employs AI algorithms to evaluate data on mosquito populations and environmental variables in order to forecast and stop disease outbreaks, including dengue fever and Zika. FarmSense’s patent product FlightSensor tracks pest activity around the clock, classifies insects more precisely, and uses AI-cloud data based on machine learning to improve the pest model.
The potential of pest management algorithms to increase the efficacy and efficiency of pest control measures is substantial. These algorithms can prevent crop damage, improve pesticide use, and lessen environmental impact by utilizing data analysis, predictive modelling, and real-time monitoring. However, reliable data collection, ongoing improvement, and adjustment to changing environmental factors and pest behaviours are necessary for these algorithms to succeed. To guarantee long-lasting and effective pest control results, a balance between algorithmic solutions and conventional pest management techniques is essential. By adopting the bottleneck, traditional agricultural will be transformed into automated, sustainable agriculture.
Dr. Uttam Nath
Asst. Professor & HOD
Department of Botany
St. John College,
Dimapur.