Transparent Peer Review By Scholar9

Advances in Tomato Disease Detection: A Comprehensive Survey of Machine Learning and Deep Learning Approaches for Leaves and Fruits

Abstract

Tomatoes contributed about 232 billion Indian rupees to the Indian economy in the financial year 2020; it is next to potatoes in vegetable production in South Asian countries. Tomatoes are the most familiar vegetable crop, extensively cultivated on cultivated land in India. The tropical weather of India is relevant for development, but specific weather conditions and several other features affect the standard progress of tomato plants. Besides these weather conditions and natural disasters, plant disease is a big crisis in crop production and plays a vital role in financial loss. The typical disease detection approaches for tomato crops cannot produce a predictable solution, and the recognition period for diseases is slower. A primary recognition of disease provides optimum solutions compared to the existing detection methods. Recently, distinct technologies such as AI, IoT, pattern recognition, computer vision (CV), and image processing have quickly developed and been executed for agriculture, specifically in the automation of disease and pest detection procedures. CV-based technology deep learning (DL) approaches have been performed for previous disease detection. This study proposes a wide-ranging investigation of the disease detection and classification approaches inferred for Tomato Leaf Detection. This work also reviews the advantages and disadvantages of the methods presented. Additionally, the advancements, challenges, and opportunities are discussed in this field, providing insights into the recent methods. This survey is an appreciated resource for practitioners, researchers, and stakeholders involved in tomato cultivation and agricultural technology.

Srinivasulu Harshavardhan Kendyala Reviewer
16 Oct 2024 03:18 PM

Review Request Accepted

Srinivasulu Harshavardhan Kendyala Reviewer
16 Oct 2024 03:19 PM

Not Approved Rating

Relevance and Originality

Methodology

Validity & Reliability

Clarity and Structure

Results and Analysis

Comment

Relevance and Originality:

This research addresses a significant issue in agricultural production, particularly in the context of tomato cultivation, which plays a crucial role in the Indian economy. The focus on plant disease detection using modern technologies like AI and computer vision is highly relevant, given the increasing need for efficient agricultural practices and sustainable food production. The originality of the study lies in its comprehensive exploration of various disease detection methods and their implications for tomato crops, an area that can greatly benefit from advancements in technology.

Methodology:

The methodology section should provide a clearer overview of the specific techniques employed for disease detection and classification. While it mentions the use of AI, IoT, pattern recognition, and deep learning (DL), elaborating on how these technologies were integrated into the study would enhance clarity. It would be beneficial to outline the data collection process, including the types of data used (e.g., images of tomato leaves) and any preprocessing steps undertaken. Additionally, discussing the criteria for evaluating the effectiveness of different detection methods would strengthen the methodology section.

Validity & Reliability:

To ensure the validity of the findings, it is essential to address the representativeness of the data used in the study. Information regarding the sources of the datasets, such as whether they were collected from various regions or under different conditions, would bolster reliability. Moreover, discussing how the models were validated, including performance metrics such as accuracy, precision, and recall, would provide a clearer picture of the reliability of the results. Any limitations or biases in the data should also be acknowledged to give a more balanced view.

Clarity and Structure:

The overall structure of the paper could benefit from clearer organization, with distinct sections for introduction, methodology, results, and discussion. This would allow readers to follow the flow of the research more easily. Adding subheadings within these sections can help break down complex information. Incorporating visuals, such as charts or graphs, to illustrate key findings or comparisons between methods would enhance comprehension and make the results more engaging.

Result Analysis:

The result analysis should include specific findings regarding the effectiveness of the various disease detection methods discussed. Presenting quantitative data, such as detection rates or accuracy levels of different techniques, would provide a concrete basis for comparison. Furthermore, discussing the practical implications of these findings for tomato growers and agricultural practices would enhance the research's applicability. Recommendations for future research, including potential improvements to the detection methods or exploration of other crops, would also be valuable for advancing the field of agricultural technology.