Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Một phương pháp tinh vi để ước lượng giá cây trồng tương lai nhấn mạnh mô hình học máy và học sâu
Tóm tắt
Dự đoán giá cho các loại cây trồng tưới nhỏ giọt, chẳng hạn như rau quả, có ý nghĩa quan trọng đối với nông dân, thương nhân và người tiêu dùng. Một dự đoán giá hợp lý và chính xác cho phép nông dân đưa ra quyết định sáng suốt về việc chọn lựa những thị trường gần đó để bán sản phẩm của họ, tối đa hóa cơ hội đạt được giá cả thuận lợi. Nông dân có thể sử dụng thông tin được cung cấp để đưa ra quyết định sáng suốt về thời điểm tối ưu cho các chiến lược tiếp thị của họ. Nhiều mô hình thống kê đã được sử dụng trong quá khứ để dự đoán giá các mặt hàng nông sản. Tuy nhiên, việc thừa nhận rằng các mô hình này bị ràng buộc bởi một số giới hạn liên quan đến các giả định cơ bản của chúng là rất quan trọng. Chúng tôi đã áp dụng mô hình hồi quy, một kỹ thuật phân loại học máy, và bộ nhớ dài-ngắn hạn, một kỹ thuật học sâu để dự đoán giá cây trồng. Các tham số để dự đoán giá bao gồm loại cây trồng, giá trị dinh dưỡng, giá hỗ trợ tối thiểu, giống cây trồng và vị trí. Nghiên cứu này đề xuất và thực hiện một cơ chế để dự đoán giá cây trồng từ dữ liệu lịch sử. Các biến đầu vào được chọn được sử dụng để phát triển các mô hình hồi quy và bộ nhớ dài-ngắn hạn. Hiệu suất của các mô hình này được đánh giá bằng các chỉ tiêu đánh giá chuẩn, bao gồm Lỗi bình phương trung bình, Lỗi bình phương căn và Lỗi phần trăm tuyệt đối trung bình. Dựa trên các kết quả thí nghiệm, rõ ràng rằng mô hình bộ nhớ dài-ngắn hạn thể hiện mức độ khớp tốt đáng kể và thể hiện độ chính xác cao trong việc dự đoán kết quả, vượt qua các mô hình khác và nghiên cứu trước đây.
Từ khóa
#dự đoán giá #cây trồng #học máy #học sâu #mô hình hồi quy #bộ nhớ dài-ngắn hạn #dữ liệu lịch sử #nông sảnTài liệu tham khảo
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