Automatic Pesticide Suggestion by Detecting the Plant Leaf Diseases
Prof. R.A. Nikam
Information Technology department of Anantrao Pawar college of Engineering and research, Parvati, Pune. Savitribai Phule Pune University.
rajashri.nikamabmspapcoerpune.org
Namrata Bandrupe
Information Technology department of Anantrao Pawar college of Engineering and research, Parvati, Pune.
Savitribai Phule Pune University.
nbandrupe@gmail.com
Vaishanvi Shamod
Information Technology department of Anantrao Pawar college of Engineering and research, Parvati, Pune. Savitribai Phule Pune University.
shamodvaishnavi@gmail.com
Pallavi Shelke
Information Technology department of Anantrao Pawar college of Engineering and research, Parvati, Pune. Savitribai Phule Pune University.
Shwelkepallavi655@gmail.com
Swapnali Hirole
Information Technology department of Anantrao Pawar college of Engineering and research, Parvati, Pune. Savitribai Phule Pune University. swapnalihirole456@gmail.com
Abstract— Plant disease has a major impact on agricultural productivity, causing economic losses. Conventional practices of disease recognition and pesticide selection are based on visual inspection and the expertise of experienced individuals and hence are time-consuming and subject to errors. This paper reports an automated disease detection system for plant leaves and pesticide suggestion through image processing and machine learning. The system takes images of leaves, analyzes them with deep learning techniques to detect diseases, and finally recommends best pesticides from an expert database. With the help of artificial intelligence, it maximizes accuracy, minimizes reliance on human knowledge, and facilitates early intervention, thereby enhancing crop yield and sustainability. The experimental results prove the model's capability to detect prevalent plant diseases accurately and also offer accurate pesticide suggestions. This system has huge potential to be integrated in smart farming solutions and is presented as a cost-effective and scalable tool available for farmers globally. Keywords— Plant Disease Detection, Pesticide Recommendation, Machine Learning, Deep Learning, Image Processing, Smart Agriculture, Precision Farming.
