The lack of transparency and interpretability in deep

The gap

Evidence profile

Sourced from the future work and inline gaps and stated research gap of the source papers, classified as general, spanning 4 journals.

Supporting evidence — 4 representative gaps

• Deep learning and IoT-based framework for sesame plant identification and weed detection (2026) · Scientific Reports · doi

  Identifying weeds correctly is important for keeping plants healthy in the future and getting the most out of your farm. This paper presents a novel methodology for weed identification, leveraging the capabilities of deep learning algorithms to facilitate streamlined weed detection. We used a tailored model with an RCNN architecture. It can help make boundary boxes for both weeds and crops, which makes it easier to tell the difference between ARTICLE IN PRESSARTICLE IN PRESS ACCEPTED MANUSCRIPT the two. The analysis shows that it works very well because the highest bounding box accuracy is 0.720, the lowest bounding box accuracy is always 0.221, and the validation class accuracy can be as high as 0.950. The suggested procedures make it easy to accurately identify weeds in crop fields. The dataset (around 1300 photos) was carefully labeled and shows typical field situations, making it a good proof-of-concept. The results show how region-based CNNs and advanced object detection techniques may work together for precision agriculture. Future work will explore one-stage detectors such as YOLO for deployment-critical scenarios.The possibility of employing a Region Proposal Network (RPN) in lieu of selective search presents a prospective avenue for future research, highlighting its capacity to enhance runtime efficiency. Modern end-to-end models outperform CNN+SVM in speed, but the proposed approach offers analytical flexibility. By adding IoT sensor data, the system may be made to give irrigation advice that takes into account the moisture level of the soil. Also, using Internet of Things sensors in the last few years will make this method even better at assessing soil moisture levels and pest populations, which will improve crop management even more. Conflict of interests: There is no conflict of interests among students: Authors contributions: P.N. and V.M. conceived the study and designed the methodology. K.M.S. implemented the software, performed the formal analysis, and prepared the visualizations. S.A.K.S.A. and S.M. carried out the investigation and curated the data. C.K.C. and C.S. provided resources, contributed to validation, and supervised the work. P.N. and K.M.S. drafted the manuscript, and V.M., S.A.K.S.A., S.M., C.K.C., and C.S. reviewed and edited it. P.N. coordinated the project. All authors approved the final manuscript. Data availability statement: ARTICLE IN PRESSARTICLE IN PRESS ACCEPTED MANUSCRIPT All data used in the manuscript is within the manuscript itself. Acknowledgement: NIL Funding: No Funding is received.

  generalfuture workevidence 5/5
  Keywords: manuscript article press weeds future make accuracy presents methodology weed detection used accepted shows bounding
• Image Processing-Based Detection of Pomegranate Leaf Diseases Using K-Means Clustering and SVM (2026) · International Journal of Creative and Open Research in Engineering and Management · doi

  Future work will focus on expanding the dataset to include a wider variety of leaf disease stages and environmental conditions, integrating deep learning architectures such as Convolutional Neural Networks (CNNs) to improve detection robustness and accuracy, and deploying the system as a lightweight mobile or IoT-based application to enable real-time, field-level leaf disease diagnosis. The findings of this study confirm that image processing and machine learning techniques offer a viable and effective alternative to conventional manual leaf inspection methods, which are time- field varying conditions, Despite its promising performance, the system has certain limitations that warrant further investigation.

  generalinline gapsevidence 5/5
  Keywords: leaf disease conditions learning system time field future focus expanding dataset include wider variety stages
• COMPARATIVE PERFORMANCE AND GENERALIZATION ANALYSIS OF MOBILENETV1 AND MOBILENETV2 FOR RHIZOME SPICE CLASSIFICATION (2026) · Jurnal Pertanian Presisi (Journal of Precision Agriculture) · doi

  Furthermore, the Future work may focus on proposed system shows potential to expanding the dataset with additional support agricultural activities, particularly rhizome spice classes and more diverse in assisting users in identifying rhizome image conditions, as well as investigating spices more efficiently and reducing other lightweight deep learning misidentification.

  generalinline gapsevidence 5/5
  Keywords: rhizome future focus proposed system shows potential expanding dataset additional support agricultural activities particularly spice
• Explainable Hybrid CNN-Transformer Frameworkfor Papaya Leaf Disease Classification withLayer-Wise Grad-CAM Analysis (2026) · AIUB Journal of Science and Engineering (AJSE) · doi

  The lack of transparency and interpretability in deep learning models for plant disease detection limits their adoption by agricultural practitioners. The need for a systematic approach to capturing progressive feature evolution from edge detection through texture analysis to disease-region localization. The limited evaluation of deep learning models for plant disease detection in real-world field settings.

  generalstated research gapevidence 5/5
  Keywords: lack transparency interpretability deep learning models plant disease

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