• Pooja Gedam
• Prof. Jeetendra Singh Yadav

Artificial Intelligence offers vast opportunities for application in agriculture; there still exists a lack of familiarity with high tech machine learning solutions in farms across most parts of the world. AI systems also need a lot of data to train machines and to make precise predictions. Tomatoes (Solanum lycopersicum) can be grown on almost any moderately well-drained soil type. This research presents an adaptive machine learning approach for image based plant leaf disease identification with performance improvement. Simulation is performed using Python sypder 3.7 version. The overall accuracy is achieved 98% in different plant leaf disease identification.

Sypder, Python, Accuraccy, AL, Plant, Disease, Machine Learning

"An Adaptive Machine Learning Approach for Image Based Plant Leaf Disease Identification with Performance Improvement", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 9, page no.e716-e720, September 2021, Available :http://www.jetir.org/papers/JETIR2109485.pdf

"An Adaptive Machine Learning Approach for Image Based Plant Leaf Disease Identification with Performance Improvement", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 9, page no. ppe716-e720, September 2021, Available at : http://www.jetir.org/papers/JETIR2109485.pdf