Easy Agriculture: Crops’ disease detection, pesticide, fertilizer and crop recommendations
Around the world due to pests and pathogens almost 50% of the agricultural produce is lost which is so alarming given the fact that many people die everyday due to starvation in poor nations. Crop diseases disturb the normal growth and physiological processes. It is estimated that every year 20-40%...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/28/e3sconf_icmed-icmpc2023_01055.pdf |
| _version_ | 1797807763329384448 |
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| author | Kolagani Ravikiran Reddy Venkatram Jagadeeshwar Kemmasaram Varshith Sai Ravilla Gagan Vadla Vishwateja Reddy Julakanti Sai Ketan |
| author_facet | Kolagani Ravikiran Reddy Venkatram Jagadeeshwar Kemmasaram Varshith Sai Ravilla Gagan Vadla Vishwateja Reddy Julakanti Sai Ketan |
| author_sort | Kolagani Ravikiran |
| collection | DOAJ |
| description | Around the world due to pests and pathogens almost 50% of the agricultural produce is lost which is so alarming given the fact that many people die everyday due to starvation in poor nations. Crop diseases disturb the normal growth and physiological processes. It is estimated that every year 20-40% of crop loss is reported and, in some cases, whole production gets destroyed. So, to produce higher yield and for sustainable agriculture it is important to identify any diseases from the early stage itself. Technology can do a great help in this cause to detect plant disease by using various AI techniques. It is also important to recommend proper pesticides for the persisting disease. The model proposed is based upon a 9 layer resnet deep learning algorithm that takes in present time images of various crops and detects the disease & also recommends the suitable pesticide. Plant Village Dataset taken from Kaggle comprising 87000 images (38 Classes,13 Crops) is used. A custom dataset is also built consisting of disease-description-measures to be taken-pesticide or fertilizer to be used. The end system developed also has two other models integrated that are used for crop and fertilizer recommendations. They are built using the Random Forest Classifier algorithm and a parameter conditional statements function. |
| first_indexed | 2024-03-13T06:27:30Z |
| format | Article |
| id | doaj.art-1595c8cf5f844fe596b8dbc18865bf38 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-13T06:27:30Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-1595c8cf5f844fe596b8dbc18865bf382023-06-09T09:12:17ZengEDP SciencesE3S Web of Conferences2267-12422023-01-013910105510.1051/e3sconf/202339101055e3sconf_icmed-icmpc2023_01055Easy Agriculture: Crops’ disease detection, pesticide, fertilizer and crop recommendationsKolagani Ravikiran0Reddy Venkatram Jagadeeshwar1Kemmasaram Varshith2Sai Ravilla Gagan3Vadla Vishwateja4Reddy Julakanti Sai Ketan5Department of Information Technology, GRIETDepartment of Information Technology, GRIETDepartment of Information Technology, GRIETDepartment of Information Technology, GRIETDepartment of Information Technology, GRIETDepartment of Information Technology, GRIETAround the world due to pests and pathogens almost 50% of the agricultural produce is lost which is so alarming given the fact that many people die everyday due to starvation in poor nations. Crop diseases disturb the normal growth and physiological processes. It is estimated that every year 20-40% of crop loss is reported and, in some cases, whole production gets destroyed. So, to produce higher yield and for sustainable agriculture it is important to identify any diseases from the early stage itself. Technology can do a great help in this cause to detect plant disease by using various AI techniques. It is also important to recommend proper pesticides for the persisting disease. The model proposed is based upon a 9 layer resnet deep learning algorithm that takes in present time images of various crops and detects the disease & also recommends the suitable pesticide. Plant Village Dataset taken from Kaggle comprising 87000 images (38 Classes,13 Crops) is used. A custom dataset is also built consisting of disease-description-measures to be taken-pesticide or fertilizer to be used. The end system developed also has two other models integrated that are used for crop and fertilizer recommendations. They are built using the Random Forest Classifier algorithm and a parameter conditional statements function.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/28/e3sconf_icmed-icmpc2023_01055.pdf |
| spellingShingle | Kolagani Ravikiran Reddy Venkatram Jagadeeshwar Kemmasaram Varshith Sai Ravilla Gagan Vadla Vishwateja Reddy Julakanti Sai Ketan Easy Agriculture: Crops’ disease detection, pesticide, fertilizer and crop recommendations E3S Web of Conferences |
| title | Easy Agriculture: Crops’ disease detection, pesticide, fertilizer and crop recommendations |
| title_full | Easy Agriculture: Crops’ disease detection, pesticide, fertilizer and crop recommendations |
| title_fullStr | Easy Agriculture: Crops’ disease detection, pesticide, fertilizer and crop recommendations |
| title_full_unstemmed | Easy Agriculture: Crops’ disease detection, pesticide, fertilizer and crop recommendations |
| title_short | Easy Agriculture: Crops’ disease detection, pesticide, fertilizer and crop recommendations |
| title_sort | easy agriculture crops disease detection pesticide fertilizer and crop recommendations |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/28/e3sconf_icmed-icmpc2023_01055.pdf |
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