Analysis Of Classifiers With Different Attributes Related With Agriculture Patterns Using Deep Learning

Pranita D. Sherkhane; Manisha N. Jagdale; Sudarshan G. Lakhdive; Dr. Annasaheb Bhagwant Nimbalkar; Dr. Nayana Ratnaparkhi

doi:10.29284/ijasis.12.1s.2026.109-121

Authors

Pranita D. Sherkhane Research Scholar, Swami Ramanand Teerth Marathwada University Nanded, Maharashtra, India
Manisha N. Jagdale Assistant Professor, Head, Dept.of Computer Science, Waghire College of Arts, Commerce and Science Saswad, Maharashtra, India
Sudarshan G. Lakhdive Assistant Professor, Head, Dept.of Computer Science, Prof. Ramkrishna More Arts, Commerce and Science College, Akurdi, Maharashtra, India
Dr. Annasaheb Bhagwant Nimbalkar Assistant Professor, Waghire College of Arts, Commerce and Science, Saswad, Maharashtra
Dr. Nayana Ratnaparkhi Assistant Professor, DSM College, Jintur, Maharashtra, India

DOI:

https://doi.org/10.29284/ijasis.12.1s.2026.109-121

Keywords:

Deep Learning, Convolutional Neural Network (CNN), Crop Classification, Yield Prediction, Harvest Timing, Smart Agriculture, CNN+LSTM, Precision Farming, Explainable AI, Multimodal Data Fusion

Abstract

The increasing demand for sustainable and efficient agricultural practices has accelerated the integration of artificial intelligence (AI), particularly deep learning, into modern farming systems. This study presents a comprehensive analysis of various deep learning classifiers—specifically CNN-based architectures—applied to key agricultural tasks, including crop classification, yield prediction, and harvest timing. Using both open-source and field-collected datasets encompassing image, climatic, and soil data, multiple models such as VGG16, ResNet50, InceptionV3, and a hybrid CNN+LSTM were evaluated for their effectiveness. The CNN+LSTM model consistently demonstrated superior performance, achieving the highest accuracy in classification and harvest timing, and the lowest error in yield estimation. These findings underscore the importance of combining spatial imagery with temporal environmental inputs for precision farming applications. The study also explores the practical relevance of deploying these models in real-world smart agriculture systems, while emphasizing future directions involving explainable AI, multimodal data fusion, and mobile-edge deployment for broader accessibility. Overall, this research contributes to the growing field of AI-driven agriculture by identifying robust, scalable models capable of enhancing decision-making processes across diverse agro-ecological settings.

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