Abstract: This compilation of research papers delves into various facets of agricultural technology, offering innovative solutions to diverse challenges. The papers cover topics such as optimized fruit recognition through Evolution Strategy, a smart agriculture system for automatic fruit grading, and an Ensemble Machine Learning Model to identify healthy and rotten fruits, reducing global food waste. Methodological approaches using technologies like Convolutional Neural Networks and infrared technology are presented for tasks such as determining harvest time, disease detection, and quality assessment. The research also explores climate change's impact on fruit tree phenology and proposes an information architecture for the BC Tree Fruit industry to enhance Precision Agriculture adoption. Collectively, these papers contribute significantly to sustainable and efficient farming practices, addressing crucial issues in the agricultural sector.

Keywords: Automatic Food Grading, Ensemble Machine Learning Model, Infrared Technology, Quality Assesment, Image Saliency, Clima Tree Tool, Sustainable Farming Practices, Phenological Stages.

Cite:
Anush.R, Pavan.S, Prashanth.T, Revanth.N.Mithra, Roopa K Murthy, "A SURVEY ON FRUIT DATABASE", IARJSET International Advanced Research Journal in Science, Engineering and Technology, vol. 11, no. 1, 2024, Crossref https://doi.org/10.17148/IARJSET.2024.11117.

| DOI: 10.17148/IARJSET.2024.11117