IPM: CALEW Cotton: an integrated expert system for cotton production and management (original) (raw)
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Successful decisions largely depend on correct interpretation of data. Today, our ability to collect data outstrips our ability to interpret it, a situation called "information input overload." Information input overload is known to have a deleterious effect on decision makers. Full use of data, knowledge, and other information requires a system that can extract the critical decision factors and follow a decision tree to find related pieces of information. A knowledge-based system was built to aid the project management team responsible for identifying cotton fields at risk to pink bollworm and releasing sterile pink bollworm to help-/control the native pink bollworm population. The system uses object-oriented design, expert system techniques, a link to simulation models, and database management in an integrated system to optimize, improve, and ease the decision-making process. The system made significantly fewer mistakes than did human decision makers, while assigning treatments to high and low risk areas. In addition, the system thoroughly documents the decisionmaking process and the resulting recommendations, thus allowing use of adjuncts such as a GIS and simulation models of pest and crop populations.
Computer-based cotton pest management in Australia
Field Crops Research, 1981
A prototype pest management system for cotton incorporating data handling and decision making by computer was progressively modified during 1978 and 1979 to increase its efficacy and practical feasibility. A more realistic assessment of crop status was developed. Threshold population densities of pests were revised. Labour requirements were reduced by sequentially sampling insects on terminals three times a week and by simulation of fruit development during week-long intervals between plant sampling. Decision making was refined using recent experience. The developing system, tested in 1978-79 on a 14 ha field and in 1979-80 on 360 ha of cotton grown on four farms, maintained yields at commercial levels although insecticide usage was decreased by 40%.
A framework of an expert system for crop pest and disease management
Journal of theoretical and applied information technology, 2013
Crop pest and disease diagnosis are amongst important issues arising in the agriculture sector since it has significant impacts on the production of agriculture for a nation. The applying of expert system technology for crop pest and disease diagnosis has the potential to quicken and improve advisory matters. However, the development of an expert system in relation to diagnosing pest and disease problems of a certain crop as well as other identical research works remains limited. Therefore, this study investigated the use of expert systems in managing crop pest and disease of selected published works. This article aims to identify and explain the trends of methodologies used by those works. As a result, a conceptual framework for managing crop pest and disease was proposed on basis of the selected previous works. This article is hoped to relatively benefit the growth of research works pertaining to the development of an expert system especially for managing crop pest and disease in ...
Qeios, Jaunary 26 , 2024
Artificial intelligence (AI)-based pest management advisory, based on integrated pest management (IPM), provided to cotton farmers on smartphones, resulted in a reduction in pest attacks & up to 22% higher income in the 1 year 2020-21 in Ranebennur, Karnataka, and Wardha, Maharashtra states. However, no significant benefit was seen in a multistate experiment in 2021-22 due to unusually high rainfall, resulting in lower pest attacks. The artificial intelligence was used in pest detection & counting insect numbers in the pheromone trap to decide if threshold numbers were reached for pesticide spraying decisions. This was 1-2 weeks in advance of mass pest emergence and could control it to reduce crop damage. It required manual trap checking by the farmers on a weekly basis, which many farmers disliked. Artificial intelligence coupled to remote sensing, GIS, and/or farm sensors can benefit the farmers by cutting costs, increasing yield, and enabling cleaner production. Lower environmental pollution and less risk to farmers and consumers are cobenefits of the AI-IPM package. However, mating disruption technology, a competitor, includes putting 4-6 pheromone traps per acre for the mass capture of moths. It is organic-compatible, and another competitor is the mechanical growing degree day (GDD)-based IPM advisory, such as that provided by the startup "Fasal." These are unintelligent, mechanical, but very effective algorithms. Thus, a cautious, logical, and gradual approach is needed in promoting AI in agriculture, also keeping in mind its impact on labour displacement.
Pest Management In Cotton Farms
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020
Figure 1: Pesticide spray recommendation from pest trap images using AI. We introduce an AI-based method for pest monitoring that is geared towards smallholder farmers. Using photos of pest traps in the field, the system provides a recommendation on whether to spray pesticide. This paper outlines that system along with the lessons learned during its deployment in the context of global development.
Pest Management Science, 2003
We present an overview of USDA Agricultural Research Service (ARS) computer models and databases related to pest-management science, emphasizing current developments in environmental risk assessment and management simulation models. The ARS has a unique national interdisciplinary team of researchers in surface and sub-surface hydrology, soil and plant science, systems analysis and pesticide science, who have networked to develop empirical and mechanistic computer models describing the behavior of pests, pest responses to controls and the environmental impact of pestcontrol methods. Historically, much of this work has been in support of production agriculture and in support of the conservation programs of our 'action agency' sister, the Natural Resources Conservation Service (formerly the Soil Conservation Service). Because we are a public agency, our software/database products are generally offered without cost, unless they are developed in cooperation with a private-sector cooperator. Because ARS is a basic and applied research organization, with development of new science as our highest priority, these products tend to be offered on an 'as-is' basis with limited user support except for cooperating R&D relationship with other scientists. However, rapid changes in the technology for information analysis and communication continually challenge our way of doing business. Published in
Agricultural Systems, 2003
This paper presents a DIagnostic Advisory Rule-based Expert System for Integrated Pest Management (DIARES-IPM) in Solanaceous crops. DIARES-IPM is an operational automatic identification tool that helps non-experts to identify pests (insects, diseases, nutritional deficiencies and beneficial insects) and suggest the appropriate treatments. The objective of this expert system was to serve as a diagnostic, extension and educational tool in vegetable IPM and it includes the most economically important diseases, insects (noxious and beneficial insects) and nutritional deficiencies that affect these crops. All the diagnostic knowledge is contained in an integrated knowledge base. This is of great importance for IPM, in which all the pests are to be taken into account if an appropriate management strategy is to be applied. The methodology can also be applied to other vegetable crops without needing to rewrite the core knowledge base, while the overall system design, with minor changes, could be applicable to pest diagnosis or pest risk assessment in any other agro-ecosystem. To implement DIA-RES-IPM, EXSYS tool for Windows was used and the knowledge is represented in the linguistic form of IF-THEN rules. The expert system has been evaluated following conventional expert system evaluation methodologies.
Agricultural applications of expert systems concepts
Agricultural Systems, 1989
In the last four years there has been considerable interest among agriculture researchers in the concept of expert systems. Although initial enthusiasm was high and many projects were initiated the number of fielded agricultural expert systems remains low. This paper reviews some of the systems that have demonstrated their viability and considers their shared characteristics. The pattern among these viable expert systems involves careful attention to well defined objectives, especially in regard to each project's requirement for knowledge acquisition. The primarily heuristic expert systems focus on very narrow domains, as do the real-time expert controls projects. Other systems leverage the value of their heuristic knowledge by providing increased access to existing models and/or databases. In general the projects reviewed leave little doubt that expert systems concepts can be successfully used to expand the capabilities of specific agricultural software applications.
A Web-Based Decision Support System for Managing Greenbugs in Wheat
cm, 2004
IPM creates synergies by integrating complementary methods drawing from a diverse array of approaches that include biocontrol agents, plant genetics, cultural and mechanical methods, biotechnologies, and information technologies, together with some pesticides that are still needed to control the most problematic pests and to manage critical situations. Concepts of IPM, IP, and IF are based on dynamic processes and require careful and detailed organisation and management of farm activities at both strategic and tactical levels. This means that time must be invested in management, business planning, data collection and detailed record keeping, and identification of required skills and provision for appropriate training to ensure safe farm operation. In IPM, IP, and IF, farm managers must also know where to obtain expert advice, and they must be willing to accept scientific and technical advances that benefit the environment, food quality, and economic performance, and that therefore can be integrated into the crop management as soon as they are reliable (EISA, 2001).