AN ANALYTICAL STUDY ON ARTIFICIAL INTELLIGENCE IN AGRICULTURE SECTOR OF MALNAD AREA OF KARNATAKA

ABSTRACT

Malnad (Malenadu) is a region in the state of Karnataka in India. Malenadu covers the western and eastern slopes of the Western Ghats or Sahyadri mountain range, and is roughly 100 kilometers in width. The region experiences heavy annual rainfall of 1000 to 3800 mm; it includes Agumbe, which receives the highest annual rainfall in Karnataka (over 10,000 mm). In Malnad area the villages are scattered to lying in remote areas. This region in the state poses special problems of development mainly due to peculiar settlement, sparse population, topography, dense forest, numerous rivulets etc. Areca nut is a tropical crop which is also known as betel nut. India is second in both areca nut production and consumption worldwide. A variety of diseases affect it throughout its life cycle from root to fruit. Cultivation classification is among the most crucial processes in crop management. Classification might be advantageous for different grades. Several textural features are extracted from the areca nut using Wavelet, Gabor, Gray Level Difference Matrix (GLDM), and Gray Level Co-Occurrence Matrix (GLCM). Currently, disease detection is done purely by visual observation, and farmers must carefully analyze each crop on a regular basis to detect diseases. A picture is entered into a convolutional neural network (CNN), a deep learning algorithm, which then assigns learnable weights and biases to various objects in the image, and then, based on the outcomes, learns to differentiate between them.

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