Artificial intelligence in agriculture
- 2. INTRODUCTION Artificial Intelligence is a branch of computer science dealing with the simulation of intelligent behavior in computers. “Artificial Intelligence is not a Man versus Machine saga; it’s in fact, Man with Machine synergy.”
- 3. GREEN REVOLUTION The global population is expected to reach 10 billion people by 2050, which means double agricultural production in order to meet food demands which is about 70% increase in food production. Farm enterprises require new and innovative technologies to face and overcome these challenges. By using A I we can resolve these challenges.
- 4. H0W A I IS USED IN AGRICULTURE: Automated farming activities. Identification of pest and disease outbreak before occurrence. Managing crop quality. Monitoring biotic. Abiotic factors and stress. Machine vision systems and phenotype lead to adjustments.
- 6. AUTOMATED IRRIGATION SYSTEM: EFFECT OF USAGE: Reducing production costs of vegetables, making the industry more competitive and sustainable. Maintaining (or increasing) average vegetable yields Minimizing environmental impacts caused by excess applied water and subsequent agrichemical leaching. Maintaining a desired soil water range in the root zone that is optimal for plant growth. Low labor input for irrigation process maintenance Substantial water saving compared to irrigation management based on average historical weather conditions.
- 7. AI-REMOTE SENSING: CROP HEALTH MONITORING: Hyperspectral imaging and 3D Laser Scanning, are capable of rapidly providing enhanced information and plant metrics across thousands of acres with the spatial resolution to delineate individual plots and/or plants and the temporal advantage of tracking changes throughout the growing cycle.
- 8. Conventional methods are often time consuming and generally categorical in contrast to what can be analyzed through automated digital detection and analysis technologies categorized as remote sensing tools. The trained use of hyperspectral imaging, spectroscopy and/or 3D mapping allows for the substantial increase in the number of scalable physical observables in the field . In effect, the multi sensor collection approach creates a virtual world of phenotype data in which all the crop observables become mathematical values.
- 9. AI FOR HARVESTING VINE CROPS: Conventional methods are often time consuming and generally categorical in contrast to what can be analyzed through automated digital detection and analysis technologies categorized as remote sensing tools. The trained use of hyperspectral imaging, spectroscopy and/or 3D mapping allows for the substantial increase in the number of scalable physical observables in the field In effect, the multi sensor collection approach creates a virtual world of phenotype data in which all the crop observables become mathematical values.
- 10. AI FOR AUTONOMOUS EARLY WARNING SYSTEM FOR ORIENTAL FRUIT FLY (BACTROCERA DORSALIS) OUTBREAKS This autonomous early warning system, built upon the basis of wireless sensor networks and GSM networks effectively captures long-term and up-to- the-minute natural environmental fluctuations in fruit farms. In addition, two machine learning techniques, self- organizing maps and support vector machines, are incorporated to perform adaptive learning and automatically issue a warning message to farmers and government officials via GSM networks
- 12. DECISION SUPPORT SYSTEM (DSS) FOR FIELD PREDICTION USING AI TECHNIQUES • This system involves a set of Artificial Intelligence based techniques: • Artificial Neural Networks (ANNs) • Genetic Algorithms (GAs) • Grey System Theory (GST). • Use of artificial intelligence based methods can offer a promising approach to yield prediction and compared favorably with traditional methods.
- 13. AI -DRIVER LESS TRACTOR Using ever-more sophisticated software coupled with off-the-shelf technology including sensors, radar, and GPS, the system allows an operator working a combine to set the course of a driverless tractor pulling a grain cart, position the cart to receive the grain from the combine, and then send the fully loaded cart to be unloaded.
- 15. AI FOR WEEDING • The Hortibot is about 3-foot-by-3- foot, is self-propelled, and uses global positioning system (GPS). It can recognize 25 different kinds of weeds and eliminate them by using its weed- removing attachments
- 16. • HortiBotis eco-friendly, because it sprays exactly above the weeds • As the machine is light --between 200 and 300 kilograms --so it will not hurt the soil behind it. • It is also cheaper than the tools currently used for weed-elimination as it can work during extended periods of time.
- 18. CONCLUSION AI can be appropriate and efficacious in agriculture sector as it optimises the resource use and efficiency. It solves the scarcity of resources and labour to a large extent. Adoption of AI is quite useful in agriculture. Artificial intelligence can be technological revolution and boom in agriculture to feed the increasing human population of world. Artificial intelligence will complement and challenge to make right decision by farmers.