INSECT, DISEASE DETECTION AND CLASSIFICATION IN FIELD TOMATO CROP USING MACHINE LEARNING.
Anjali Sandip Kumbhar
, Prof. V. D. Mhaske , Sakshi Vishnu Khatal , Sakshi Hanumant Karande , Apeksha Changdev Chavan
Insect Detection, Disease Detection, Field Crops, Machine Learning, Crop Classification, Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Image Processing, Feature Extraction, Agricultural Technology, Pest Detection, Smart Farming, Early Disease Detection, Precision Agriculture.
Tomato is one of the most widely cultivated and essential crops globally. Its yield can be significantly impacted by plant diseases and inadequate treatments. Various factors such as climate conditions, weather fluctuations, natural calamities, and plant infections contribute to reduced agricultural productivity, leading to substantial economic losses. In this regard, this study conducts a comparative analysis of tomato leaf disease diagnosis using machine learning (ML) methodologies. A comprehensive review of existing models is carried out to determine the most efficient approach that delivers superior accuracy. An ML model based on InceptionV3 is proposed to facilitate the early detection of tomato leaf disorders and prevent further disease progression. The proposed approach achieves a classification accuracy of 81%. Tomato is one of the most important and widely grown crops worldwide. Its production can be severely affected by plant diseases and incorrect treatments. Factors like climate changes, weather conditions, natural disasters, and infections also reduce crop yield and cause economic losses.
"INSECT, DISEASE DETECTION AND CLASSIFICATION IN FIELD TOMATO CROP USING MACHINE LEARNING.", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.12, Issue 4, page no.a512-a516, April-2025, Available :https://tijer.org/TIJER/papers/TIJER2504074.pdf
Volume 12
Issue 4,
April-2025
Pages : a512-a516
Paper Reg. ID: TIJER_157454
Published Paper Id: TIJER2504074
Downloads: 000287
Research Area: Science and Technology
Country: Baramati, Maharashtra, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
