• SHRIDHARSHINI M
• SWETHA R
• VAISHNAVI D
• SOWBARNIGA A

Considering the recent rate of population expansion, it is anticipated that by 2050, global crop productivity will need to increase by twofold. One of the biggest barriers to reaching this productivity goal is diseases and pests. Thus,it is crucial to create effective techniques for the automatic identification, detection, and forecasting of agricultural crops' pests and illnesses. Machine Learning (ML) strategies can be used to extract relationships and information from the data being worked on in order to execute such automation. This study reviews the literature on machine learning (ML) applications in the agricultural sector, with a particular emphasis on tomato crops. It focuses on the tasks of disease and pest classification, detection, and prediction. This research aims to promote the development of precision agriculture and smart farming by encouraging the creation of methods that will enable farmers to reduce their usage of chemical pesticides and herbicides while maintaining and raising the productivity and quality of their crops.

Agriculture; plant diseases; machine learning; detection and classification; dataset collection; smart farming

"RESEARCH ON PLANT LEAVES DISEASE DETECTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.f853-f859, June-2024, Available :http://www.jetir.org/papers/JETIR2406598.pdf

"RESEARCH ON PLANT LEAVES DISEASE DETECTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppf853-f859, June-2024, Available at : http://www.jetir.org/papers/JETIR2406598.pdf